Forem: Aakash R

Top 10 Agent Skills Every Developer Should Install 🦾🛠️

Aakash R — Tue, 14 Apr 2026 08:04:56 +0000

AI agents are easy to demo, but getting them to work in real use is a different challenge.

Many setups can give good answers. Very few can finish a task, deal with errors, and keep track of what is going on across steps. To build that kind of agent, you need the right skills, such as using tools, managing memory, handling data, deploying your setup, and shaping how people use it.

This article covers the top 10 skills you need to build AI agents that actually work outside demos. Let’s get started!

1. Tool Integration with Composio

Composio helps your agent connect to external tools and APIs without dealing with complex setup. You can plug your agent into 1000+ tools like Gmail, Slack, GitHub, and more, and start executing real actions quickly.

Composio’s skill pack is built from real production use. It handles things that most agents get wrong, such as tool routing, session handling, authentication, and real-time events.

What the agent learns:

Tool Routing: Picking the right tool at the right time with proper session control
Authentication Flows: OAuth, API keys, auto vs manual auth, and connection handling
Session Management: Keeping user data isolated across multi-user setups
Webhook And Trigger Handling: Creating triggers, verifying requests, and managing lifecycle
Framework Integration: Working with LangChain, OpenAI Agents SDK, CrewAI, and Claude

Why it matters:

Without this skill, agents often mix up user sessions, break authentication, or call tools out of order. These issues may not show up in testing, but they cause serious failures in real use.

Install:

npx skills add composiohq/skills

This is the layer that turns your agent from a chatbot into a system that can actually get work done.

2. Frontend And Deployment Skills with Vercel

Vercel’s agent skills focus on building and deploying fast web apps using React and Next.js. It covers performance, UX, accessibility, and deployment best practices. This is a core skill set for any agent working on frontend or full-stack projects.

What the agent learns:

React And Next.js Performance: 40+ rules across multiple areas, from fixing render waterfalls to better caching patterns
Bundle Optimization: Reducing bundle size, using dynamic imports, and avoiding unnecessary client components
Server and Client Boundary: Sending only required data across components and avoiding over-serialization

Why it matters:

Frontend is where most agent-generated code breaks first. Code may work locally but fail in real-world use due to slow load times, poor structure, or bad UX. Without this skill, agents create apps that feel slow and unstable at scale.

Install:

npx skills add vercel-labs/agent-skills

3. Memory And Retrieval Skills with Weaviate

The Weaviate agent skill pack connects your agent to Weaviate’s infrastructure and fixes a common issue where agents guess outdated syntax or misuse search settings. It helps your agent build reliable semantic search and retrieval systems without errors.

What the agent learns:

Cluster Management: Inspecting schemas, creating collections, and managing metadata
Data Lifecycle: Importing and structuring CSV, JSON, and JSONL data with clean pipelines
Semantic Search: Choosing between keyword, semantic, and hybrid search with correct tuning
Agentic Search: Using natural language queries with built-in search and citation support
Advanced Retrieval: Working with multivector embeddings and combining BM25 with vector search
End-to-End Cookbooks: Building full RAG pipelines, chat systems, and deployable services

Why it matters:

Without this skill, agents often use outdated syntax, break search queries, or miss key setup steps. With it, an agent can set up a working search system, load data, and build a usable retrieval pipeline in minutes.

Install:

npx skills add weaviate/agent-skills

4. Model And ML Workflow Skills with Hugging Face

The Hugging Face agent skill pack connects your agent to the full Hugging Face ecosystem. It supports tools like Claude Code, Codex, Gemini CLI, and Cursor, and covers the complete ML workflow from datasets to training to deployment.

What the agent learns:

HF CLI: Managing models, datasets, repositories, and authentication using tokens
Dataset Workflows: Fetching data, filtering, searching, and downloading structured datasets
Model Training: Fine-tuning with SFT, DPO, GRPO, reward models, and handling deployment formats like GGUF
Gradio UIs: Building demos, chat interfaces, and interactive web apps
HF Jobs: Running GPU workloads, batch jobs, and tracking experiments
Transformers.js: Running models directly in the browser
Paper Publishing: Managing and linking research papers with models and datasets

Why it matters:

ML workflows include many connected steps where small mistakes can break results. Issues like wrong configs, poor batching, or missed auth steps can affect training and output quality. This skill helps agents follow correct patterns and produce reliable results across the workflow.

Install:

npx skills add huggingface/skills

5. Web Data And Automation Skills with Olostep

Olostep is a web scraping, crawling, and search API built for AI workflows. The Olostep setup works through MCP, so you can plug it into any MCP-compatible agent and grant it access to real-time web data.

What the agent learns:

Single URL Scraping: Extracting content from any page in Markdown, HTML, JSON, or plain text with JavaScript support
Batch Extraction: Processing up to 100,000 URLs in parallel with structured outputs
Site Crawling: Moving across pages to collect data from full websites
Structured Parsers: Using ready-made extractors for sources like Amazon, Google Search, and Maps
Natural Language Extraction: Asking for data in plain English and getting structured results
Web Agents: Automating steps like form filling, clicking, and navigation

Why it matters:

Web data is messy, inconsistent, and often blocked. Agents that try to scrape on their own spend time on retries, broken parsing, and access issues. Olostep handles these problems and returns clean, usable data in a single step.

6. Infrastructure And Edge Skills with Cloudflare

The Cloudflare agent skill pack covers a full platform for building, deploying, and running applications at the edge. It includes compute, storage, AI tools, networking, and security, all in one place. Skills load based on what your agent is working on, so there is no manual setup.

What the agent learns:

Workers And Pages: Deploying serverless functions and static sites with proper configs, routes, and secrets
Storage: Using KV, D1, and R2 based on the type of data and access pattern
Durable Objects: Managing stateful logic, coordination, and real-time features with WebSockets and storage
AI On The Edge: Running inference, vector search, and building stateful agents with built-in tools
MCP Server Creation: Creating remote MCP servers with tool access and OAuth support
Performance: Improving load times, caching responses, and fixing render-blocking issues
Security: Setting up WAF, handling DDoS protection, and applying Zero Trust patterns

Why it matters:

Cloudflare has many moving parts and strict runtime rules. Agents often use unsupported APIs, misconfigure storage, or deploy incorrectly. This skill helps the agent follow correct patterns and build systems that run reliably at the edge.

Install:

npx skills add cloudflare/skills

7. Monitoring And Debugging Skills with Sentry

The Sentry agent skill pack is based on real patterns used by the Sentry engineering team. It is not basic documentation. It reflects how production systems are monitored, debugged, and maintained at scale.

What the agent learns:

AGENTS.md Generation: Creating and updating agent docs that match real project structure
Claude Settings Auditing: Checking configs early to catch issues before they reach production
Sentry SDK Integration: Setting up error tracking, DSNs, source maps, and capturing useful context
Error Triage Patterns: Managing issues, grouping errors, setting alerts, and tracking releases
Observability For Agents: Making workflows traceable and easy to debug with distributed tracing

Why it matters:

Agents that ship code without monitoring create failures that are hard to detect and fix. This skill ensures your agent sets up tracking from the start, so errors are visible and easier to debug in real use.

Install:

npx skills add getsentry/skills

8. Backend And Database Skills with Supabase

The Supabase agent skill pack teaches your agent how to work with a full backend platform built on Postgres. It covers database, auth, storage, real-time features, and serverless functions. It is widely used in modern full-stack apps.

What the agent learns:

Postgres Best Practices: Writing efficient queries, using indexes, and avoiding slow patterns
Row Level Security: Defining access rules correctly to protect user data
Auth Patterns: Managing sessions, JWTs, OAuth, and protected routes
Storage: Handling file uploads, access control, and signed URLs
Edge Functions: Deploying serverless logic with proper configs and secrets
Realtime: Subscribing to changes, tracking presence, and managing connections
Client Libraries: Using Supabase SDKs with proper typing and error handling

Why it matters:

Supabase is used in many production apps, but small mistakes can cause serious issues. Poor queries can slow systems down, and incorrect access rules can expose data. This skill helps your agent follow correct patterns for both performance and security.

Install:

npx skills add supabase/skills

9. Payments And Billing Skills with Stripe

The Stripe agent skill pack teaches your agent how to build secure payment flows, manage subscriptions, and handle billing systems correctly. It covers the full lifecycle of payments and helps avoid costly mistakes.

What the agent learns:

Payment Intents: Creating and confirming payments, handling authentication flows, and avoiding duplicate charges
Subscriptions And Billing: Managing recurring payments, plan changes, trials, and cancellations
Webhook Handling: Verifying signatures, handling retries, and processing events in the correct order
Connect And Payouts: Routing payments in marketplace setups and handling transfers
Fraud Detection: Using risk signals and rules to prevent fraud without blocking valid users
Test Mode Patterns: Simulating real payment scenarios and testing failure cases
Error Handling: Managing declines, network issues, and retries without breaking the flow

Why it matters:

Payments are a critical part of any product. Small mistakes can lead to failed transactions, duplicate charges, or broken billing flows. This skill helps your agent follow safe and reliable patterns from the start.

Install:

npx skills add stripe/agent-toolkit

10. Video And Media Generation Skills with Remotion

The Remotion agent skill pack teaches your agent how to create videos using code. It uses React to build, structure, and render videos, which opens up a new type of output that most agents cannot handle.

What the agent learns:

Composition Model: Structuring videos using React components, sequences, and layouts
Timeline API: Controlling animation with frame-based logic and timing functions
Rendering Pipeline: Generating videos in formats like MP4, WebM, and GIF using the CLI
Audio And Video Assets: Adding and syncing audio, clips, and visual elements
Dynamic Content: Creating videos that change based on data or inputs
Walkthrough Generation: Producing product demos and app walkthrough videos from UI flows

Why it matters:

Video creation has mostly been manual and tool-heavy. This skill allows your agent to generate videos directly from code, enabling automation of content such as demos, reports, and visual stories.

Install:

npx skills add remotion-dev/remotion

Conclusion

The agent skills ecosystem is moving fast. Fine-tuning changes how an AI behaves and is costly to maintain. Agent skills are simple instruction files. You can update, swap, or share them at any time without changing the model.

Each skill pack in this list carries real engineering knowledge. It includes production patterns, lessons from real systems, and platform-specific practices, all in a form an agent can use right away.

Install the ones that match your stack. Your agents will spend less time making errors and more time getting work done.

Top OpenClaw Integrations to Connect Your Workflow in 2026

Aakash R — Mon, 06 Apr 2026 06:48:20 +0000

If you are using OpenClaw, you likely work with multiple tools, and switching between them can quickly disrupt the flow of work. Emails, conversations, code, files, and customer data often stay spread across different platforms, so work can start to feel a bit disconnected.

OpenClaw integrations bring these tools into a more connected flow. An update in one app can carry over to another, and information stays in sync as work moves forward. This cuts down repeated steps and keeps things moving without constant back-and-forth.

Let us look at some of the most useful OpenClaw integrations across key categories and how they fit into your workflow.

OpenClaw + Composio Integration

Before getting into specific integrations, it helps to understand how OpenClaw connects with all these tools.

Composio acts as the integration layer behind OpenClaw and supports 1000+ tools across communication, development, productivity, and more. Access to this large set of apps comes through a single system, so everything feels more connected from the start.

OpenClaw integrates with Composio to manage these connections in a consistent way, which keeps each integration structured similarly. Different setups or patterns across tools are reduced, so working across them feels more straightforward.

How to Connect OpenClaw with Composio

Getting OpenClaw connected with Composio takes a few simple steps. Once set up, integrations start working through a single flow.

Step 1: Install the Composio plugin

Install the Composio plugin inside OpenClaw to begin the setup.

openclaw plugins install @composio/openclaw-plugin

Step 2: Get your Composio API key

Step 3: Add the API key to OpenClaw

Set the API key in your OpenClaw configuration.

openclaw config set plugins.entries.composio.config.consumerKey "ck_your_key_here"

Step 4: Restart OpenClaw

Restart the OpenClaw gateway to apply the changes.

openclaw gateway restart

Step 5: Authenticate your tools

When you start using tools, OpenClaw prompts authentication through Composio. Connect the apps you need, and they become available in your workflows.

After completing these steps, OpenClaw can access and trigger actions across all connected tools through Composio, setting up the foundation for your integrations.

Next, we will look at some of the most useful OpenClaw integrations across key categories.

📧 Email and Communication

Communication tools are where most work naturally starts and continues through the day. Conversations, emails, and quick updates keep things moving, but they can also get scattered across different apps.

Connecting these tools with OpenClaw keeps everything closer to your workflow. Updates can move along with the work, and conversations stay tied to the actions they relate to, which keeps things clearer as tasks progress.

1. Gmail

Emails often mark the start of a task or follow-up. Connect Gmail to your workflow, and incoming messages can turn into tasks, updates, or next steps. Important threads stay linked to ongoing activities and keep everything visible and organized.

2. Slack

Many business decisions are made in Slack. When connected to OpenClaw using Composio, the messages can directly trigger actions across your workflow. Discussions in channels can update tasks, notify team members, or move work forward as conversations progress.

3. Microsoft Outlook

Outlook is a big part of daily communication for many teams, especially for ongoing conversations and follow-ups. Emails can tie into tasks and updates, which keep important interactions easy to follow as work moves ahead. OpenClaw keeps these updates aligned across your workflow.

4. Microsoft Teams

Teams is often where collaboration happens across chats and meetings. When linked with OpenClaw, updates and actions appear alongside conversations, keeping discussions and task progress closely connected.

🛠️ Dev Tools

Development work moves across code, issues, and collaboration. Keeping these tools aligned with OpenClaw keeps progress visible and reduces the need to manually track updates across platforms.

1. GitHub

OpenClaw brings GitHub activity into your workflow, where code changes, pull requests, and issues stay visible as work moves forward. Teams often switch between repositories and task trackers to stay updated.

Repository activity can be directly tied to tasks and progress, and development updates remain clear as changes occur.

2. GitLab

GitLab brings together code, pipelines, and collaboration in one space. OpenClaw brings build activity, commits, and issue updates into your workflow, and these updates shape how progress is tracked. These updates continue into your workflow through pipeline activity and code changes, and progress becomes easier to follow as work progresses.

3. Bitbucket

Bitbucket supports teams working across repositories and code reviews, where feedback and changes happen continuously. Tracking these alongside tasks can get scattered. Pull requests and repository updates can remain tied to your workflow to keep code changes and reviews visible as work progresses.

4. Jira

Jira is widely used for managing issues, sprints, and development tasks. Progress depends on how clearly updates across tickets are tracked.

Changes in ticket status and task updates can reflect across your workflow, and this helps maintain clear and aligned progress across tasks.

🎬 Media

Media and file management often sit across storage platforms, asset libraries, and content channels. Files move through different stages, such as creation, review, and publishing, and tracking these changes across tools can take extra effort.

1. TikTok

TikTok is used for creating and sharing short-form video content. Content updates, uploads, and engagement often tie closely to campaigns and timelines. OpenClaw connects video activity with your workflow, and updates around content and publishing stay aligned with ongoing tasks.

2. Instagram

Instagram plays a key role in visual content and social engagement. Posts, reels, and updates often connect with marketing and content planning. Content activity can reflect across your workflow, and updates around publishing and engagement stay aligned with your plans.

3. X (Twitter)

X is widely used for real-time updates and audience engagement. Posts, replies, and interactions often connect with campaigns and communication strategies.

Activity on X can tie into your workflow, and updates stay aligned with ongoing content and outreach efforts.

4. YouTube

YouTube is used to host and manage video content. Uploads, edits, and performance tracking all play a role in content workflows. Video activity can span your workflow and provide better visibility into publishing timelines and how content performs within your overall process.

⚙️ Productivity

Productivity tools sit at the center of planning, tracking, and organizing work. Tasks, notes, and schedules often live in separate apps, and aligning them can take extra effort.

1. Notion

Notion is widely used for notes, documentation, and planning. Teams rely on it to organize ideas, track tasks, and manage internal knowledge. OpenClaw brings updates from Notion into your workflow, so changes in pages, tasks, or databases stay visible alongside ongoing work. This gives better clarity on how plans connect to execution.

2. Trello

Trello organizes work through boards, lists, and cards. Tasks move across stages, and tracking these changes across tools can become fragmented. Card updates and task movements can reflect in your workflow, and progress across boards stays aligned with other activities and updates.

3. Asana

Asana helps teams manage tasks, deadlines, and project timelines. Work often spans multiple teams and requires clear visibility into progress.

Task updates, status changes, and assignments can connect to your workflow, and progress across projects becomes easier to follow.

4. Google Calendar

Google Calendar manages schedules, meetings, and reminders. Events often tie directly to tasks, deadlines, and team coordination.

Calendar events and updates can reflect in your workflow, and schedules stay aligned with tasks across teams.

💼 Sales and CRM

Sales and CRM tools manage leads, customer interactions, and deal progress. Data often moves across multiple stages, and tracking updates across tools can take extra effort.

1. Salesforce

Salesforce is widely used to manage leads, accounts, and sales pipelines. Teams rely on it to track interactions and move deals through different stages. OpenClaw brings updates from Salesforce into your workflow, and changes in leads, deal stages, or customer data stay visible alongside related tasks and activities.

2. HubSpot

HubSpot supports marketing, sales, and customer engagement on a single platform. Campaigns, leads, and interactions often closely connect to ongoing work.

Updates across contacts, deals, and campaigns can reflect in your workflow, and activity across teams stays aligned as progress develops.

3. Pipedrive

Pipedrive focuses on managing deals and tracking sales pipelines. Each stage of a deal requires visibility and timely updates.

Deal updates and activity can connect to your workflow, and progress across the pipeline becomes easier to track and follow.

4. Zoho Bigin

Zoho Bigin manages customer data, interactions, and sales processes across teams. Information often needs to stay aligned across multiple touchpoints.

Updates to leads, contacts, and deals can reflect in your workflow, and customer data remains aligned with ongoing tasks and activities.

Conclusion

Work rarely stays in one tool, and information continues to move across platforms as tasks progress. OpenClaw integrations through Composio connect these tools, so updates and actions stay aligned across systems. This reduces repeated effort and adds more structure to workflows.

Across communication, development, media, productivity, and sales, integrations improve visibility and coordination. The result is a simpler way to manage work, with tools working together in a connected flow.

6 Workato Alternatives to Consider in 2026 ✅🚀

Aakash R — Fri, 06 Feb 2026 05:59:05 +0000

AI agents are being shipped to production faster than most integration layers were designed to handle. When workflows start breaking, it is usually not the model that is causing the trouble. It is authentication edge cases, permission boundaries, API limits, or long-running automations that quietly fail.

Platforms like Workato still appear early in evaluations, but teams are increasingly testing alternatives as systems become more API-driven and agent-initiated. By 2026, integrations are expected to behave like core infrastructure rather than background tooling.

This article looks at six Workato alternatives teams are actively using in 2026. The focus is on how these platforms behave in real environments, what they support well, and where trade-offs arise as workflows move beyond simple automations.

Before diving deeper, here is a quick TL;DR of the platforms worth considering.

TL;DR

If you want the quick takeaway, these are the Workato alternatives teams are actively evaluating in 2026 👇

Composio: Designed for AI agents running in production, with a large tool ecosystem, runtime execution, on-prem deployment, and MCP-native support.
Tray.ai: A good fit for complex, predefined enterprise workflows that need deep API orchestration.
Zapier: Optimized for quick, lightweight automations across common SaaS tools.
Make.com: Best for visually modeling complex, predefined workflows with branching, loops, and data transformation, especially for ops and business teams.
n8n: Ideal for teams that want full control through open-source, self-hosted automation with custom logic and deep API access.

Why a Workato Alternative Makes Sense in 2026

Integration platforms now sit directly on the execution path of modern systems. AI agents trigger actions across SaaS tools, internal services, and customer-facing workflows. Under real usage, issues around authentication, permissions, API limits, and long-running processes surface quickly.

This reality has pushed teams to look more closely at how integration tools behave beyond initial setup. Attention has shifted toward failure handling, state management, and visibility once workflows are live. These factors often determine whether a platform supports production workloads or becomes a source of operational friction.

In 2026, expectations are clear. Teams evaluating alternatives in the Workato category prioritize predictable behavior, operational control, and safe execution for agent-initiated actions over surface-level features or polished builders.

Here are the six Workato alternatives teams are actively using in 2026, along with where each one tends to fit best.

Comparison Table

Capability (vs Workato)	Composio	Tray.ai	Zapier	Make.com	n8n
Built for AI agents	Native: designed for agent tool use and action execution	No: oriented to human built workflows	Partial: can be used by agents through Zaps, not agent native	No: scenario automation, not agent focused	Partial: can power agent tools, but you assemble the patterns
Developer friendly	Native: API and SDK centric	Partial: strong platform, heavier enterprise setup	Partial: easy to start, limited deep customization	Partial: flexible builder, some developer hooks	Native: code friendly, extendable nodes, self hostable
Runtime action or tool selection	Native: pick tools dynamically at runtime	No: mostly pre defined workflow paths	No: action set is fixed at design time	No: module path is fixed at design time	Partial: possible with branching, expressions, custom logic
Managed OAuth plus automatic token refresh	Native: handles OAuth and refresh as part of connectors	Native: OAuth supported, refresh handled in connectors	Native: OAuth apps can auto refresh when configured	Native: connections handle OAuth and refresh when configured	Partial: usually supported, can vary by node and setup
Safe agent initiated actions	Native: guardrails, scoped actions, safer execution patterns	No: not built around agent safety controls	No: limited agent specific approvals or guardrails	No: limited agent specific approvals or guardrails	Partial: possible with approvals and checks you build
Long running workflows	Native: built to support longer executions and retries	Native: supports long running enterprise workflows	Partial: good for delays and scheduling, not long compute runs	Partial: supports scheduling, but scenario run time is limited	Native self hosted: configurable timeouts, Partial cloud
API first execution	Native: designed to be called and controlled via API	Partial: APIs exist, platform first	No: primarily UI driven automation	Partial: some API and webhook driven patterns	Partial: strong webhooks and APIs, depends how you deploy
Production reliability for agents	Native: built for agent execution in production settings	Partial: strong reliability, not agent specific	No: best for business automation, not agent runtimes	No: best for business automation, not agent runtimes	Partial: can be reliable, depends on hosting and ops
Self hosting	Self-hosting an =d private VPC	No: SaaS only	No: SaaS only	No: SaaS only	Native: first class self hosting option

Workato Alternatives Explained

1. Composio

Composio is a developer-first platform that connects AI agents with 500+ apps, APIs, and workflows. It is built for teams deploying agents into real production environments, where integrations need to behave predictably and survive ongoing API changes rather than just work in controlled demos.

The platform is structured around agent-initiated actions instead of static automation flows. Common integration pain points, such as authentication, permission scoping, retries, and rate limits, are managed centrally, reducing the operational overhead that typically slows teams down as systems scale.

Composio emphasizes consistency and control at the execution layer. Tools are exposed with clear schemas and stable behavior, helping agents remain reliable across long-running workflows and high-volume use cases without constant manual intervention.

Features

500+ agent-ready integrations across SaaS and internal systems
Centralized handling of OAuth, token refresh, retries, and API limits
Native Model Context Protocol support with managed servers
Python and TypeScript SDKs with CLI tooling
Works with major agent frameworks and LLM providers
Execution visibility and control for agent-triggered actions

Why is Composio a strong Workato alternative

Composio is designed for agent-driven execution where actions are selected at runtime rather than defined as static workflows. This model fits modern AI systems that need to interact with many external tools while maintaining consistent behavior around permissions, retries, and API limits.

By centralizing integration logic and exposing tools through stable, structured interfaces, Composio reduces operational overhead as systems scale. Teams can focus on agent behavior and decision-making while the platform handles execution details reliably across production environments.

Best for

Teams building AI agents that must operate across multiple services in production, especially when reliability and developer control matter more than visual workflow builders.

Benefits

Faster production readiness for agent-based systems
Reduced integration maintenance and breakage
More predictable behavior under real-world load
Cleaner separation between agent logic and tooling
Better handling of auth and API edge cases

2. Tray (Tray.ai)

Tray.ai is built for teams that need to orchestrate complex, API-heavy workflows across large SaaS environments. It is commonly used when automations span many systems and require detailed control over branching, transformations, and execution flow.

The platform is optimized for structured automation rather than agent-native execution. Workflows are typically defined upfront and refined over time, which works well for predictable processes but can introduce friction for highly dynamic, agent-driven use cases.

Features

Visual workflow builder with advanced branching and conditional logic
Deep API connectors with support for custom requests
Data mapping and transformation across steps
Built-in retries, error handling, and execution controls
Enterprise governance, access control, and security features

Why Tray is a viable alternative

Tray offers significantly more flexibility than basic iPaaS tools as workflows become more complex. Its strength lies in handling detailed API interactions and multi-step orchestration without requiring teams to build and maintain custom infrastructure.

Pros

Strong support for complex and long-running workflows
Fine-grained control over logic and execution
Well-suited for enterprise-scale automation
Reduces reliance on custom orchestration code

Cons

Less suited for highly dynamic or agent-driven execution
Setup and maintenance can be heavier than simpler tools
Visual workflows can become hard to manage at a large scale

3. Zapier

Zapier is widely used for connecting everyday SaaS tools through simple, event-driven automations. It is optimized for speed and accessibility, allowing teams to set up workflows quickly without needing deep technical knowledge or custom infrastructure.

The platform works best when workflows are short, predictable, and built around common triggers and actions. While it has added more advanced features over time, its core strength remains ease of use rather than handling complex or highly dynamic execution patterns.

Features

Thousands of prebuilt app integrations
Trigger-and-action-based workflow builder
Basic branching and filtering logic
Built-in scheduling and webhook support
Fast setup with minimal configuration

Why Zapier is a viable alternative

Zapier lowers the barrier to automation and remains a practical choice for teams that need to move quickly. For straightforward integrations and internal workflows, it often delivers results faster than heavier iPaaS platforms.

Pros

Extremely easy to use and quick to deploy
Broad integration coverage across SaaS tools
Minimal operational overhead
Accessible to non-technical teams

Cons

Limited support for complex or long-running workflows
Not well suited for agent-driven or API-heavy execution
Can become expensive at scale
Limited control over execution details

4. n8n

n8n is an open-source, developer-friendly automation platform that gives teams full control over how workflows are built, executed, and hosted. Unlike fully managed iPaaS tools, n8n can be self-hosted, making it attractive for teams that want ownership over infrastructure, data, and execution behavior.

n8n workflows are built using a node-based visual editor, but the platform is fundamentally code-capable. Teams can inject custom JavaScript logic, call arbitrary APIs, and design workflows that closely mirror real system behavior. This makes n8n flexible enough for non-standard integrations while still offering a visual layer for orchestration.

While n8n is increasingly used alongside AI systems, it is not agent-native by default. Agent-driven execution, retries, permission control, and long-running reliability must be explicitly designed and maintained by the team.

Features

Open-source core with optional managed hosting
Visual node-based workflow builder
Custom code steps with full JavaScript support
Native HTTP, webhook, and API integration nodes
Self-hosting support for security and compliance needs
Extensible via custom nodes and plugins

Why n8n is a viable alternative

n8n appeals to teams that want flexibility without vendor lock-in. By owning the execution environment, teams can tailor workflows to exact requirements, integrate deeply with internal systems, and adapt quickly as APIs or business logic change.

For organizations with engineering resources, n8n provides a powerful foundation for building bespoke automation layers that align closely with internal architecture.

Pros

Full control over execution and infrastructure
Open-source and highly extensible
Strong fit for custom and internal integrations
Suitable for self-hosted and regulated environments

Cons

Operational responsibility sits with the team
Requires engineering effort to maintain reliability
Not designed for agent-native, runtime action selection
Auth handling, retries, and governance must be built manually

5. Make.com

Make.com focuses on visual workflow orchestration for teams that need more flexibility than basic trigger-action tools, without moving fully into code-first systems. Workflows, called scenarios, are built using a drag-and-drop interface that supports branching, looping, data transformation, and conditional logic.

Make.com sits between lightweight automation tools and enterprise iPaaS platforms. It is often evaluated when teams want to model moderately complex processes across SaaS tools, internal systems, and APIs, while keeping workflows understandable to non-engineers.

The platform assumes workflows are largely defined upfront. While it supports HTTP modules and custom API calls, execution remains scenario-driven rather than agent-selected at runtime.

Features

Visual, drag-and-drop scenario builder with branching and loops
Broad SaaS integration library with custom HTTP/API modules
Data mapping, filtering, and transformation tools
Scheduling, webhooks, and event-based triggers
Execution history and basic error handling controls

Why Make.com is a viable alternative

Make.com offers significantly more control than simple automation tools while remaining accessible to operations and business teams. It allows complex logic to be expressed visually, which makes it easier to reason about workflows that span multiple systems without introducing full custom infrastructure.

For teams that want flexibility but still value visual clarity and faster iteration, Make.com can serve as a practical middle layer between no-code tools and developer-heavy platforms.

Pros

Strong visual modeling for complex workflows
More flexible logic than basic trigger-action tools
Good balance between power and usability
Suitable for cross-functional teams

Cons

Workflows must be largely predefined
Not designed for dynamic, agent-initiated execution
Limited control over deep API governance and permission boundaries
Debugging becomes harder as scenarios grow large and interconnected

Comparison Table

Capability (vs Workato)	Composio	Tray.ai	Zapier	Make.com	n8n
Built for AI agents	Yes	❌	⚠️	❌	⚠️
Developer friendly	Yes	No	No	No	Yes
Runtime action/tool selection	✅	❌	❌	❌	❌
Managed OAuth & token refresh automatically	✅	✅	⚠️	⚠️	⚠️
Safe agent-initiated actions	✅	❌	❌	❌	⚠️
Long-running workflows	✅	✅	❌	⚠️	⚠️
API-first execution	✅	⚠️	❌	⚠️	⚠️
Production reliability for agents	✅	⚠️	❌	❌	⚠️

✅ Native and well-supported
⚠️ Possible but not core
❌ Not a primary focus

Which One Should You Choose?

The right platform depends on what your system needs to optimize for. A practical way to think about the decision in 2026 is to map it to how your workflows actually behave.

Speed to production: Choose an agent-first platform with deep tool coverage, native agent protocol support, and solid SDKs.
Governance and compliance: Prioritize platforms that offer audit logs, policy controls, role-based access, and strong security guarantees.
Permission control: Look for fine-grained scopes, runtime authorization, and safe handling of agent-initiated actions.
Embedded integrations: Pick a platform designed for in-app, customer-facing integration flows with customizable UX.
Rapid experimentation: Visual builders and fast setup help validate workflows quickly.
Long-term control: Developer-centric or API-first platforms tend to scale better as systems become more complex.

A common pattern is to start with tools optimized for speed and iteration, then move to an agent-focused integration layer once workflows become production-critical.

Closing

Choosing an integration platform in 2026 comes down to how well it supports real execution, not how polished it looks in setup. As AI agents take on more responsibility inside products and internal systems, integrations need to behave predictably under load, handle edge cases cleanly, and surface failures clearly.

Each platform covered here optimizes for a different set of constraints. Composio focuses on agent-driven execution; Tray and Zapier support structured automation at different levels of complexity. Make.com excels at visually modeling complex, predefined workflows, and n8n appeals to teams that want open-source flexibility and infrastructure ownership. The right choice depends less on feature breadth and more on how closely a platform matches the way your systems actually operate in production.

Teams that evaluate these tools through the lens of reliability, control, and long-term maintenance tend to make better decisions than those optimizing for speed alone. In 2026, integration layers are no longer optional infrastructure. They are part of how systems execute.

4 Best AI Agent Authentication platforms to consider in 2026 🔐

Aakash R — Tue, 03 Feb 2026 06:45:13 +0000

AI agents in 2026 do a lot more than answer questions. They read emails, update CRMs, trigger workflows, deploy code, and quietly make changes across systems that actually matter.

That’s exciting until you realize that each of those actions requires the right access. The moment agents start taking action, authentication stops being a background detail and becomes a make-or-break part of your setup.

This is where things start to show cracks. API keys get stretched. Service accounts get reused. Permissions slowly expand as agents touch more tools and workflows. Before long, it’s hard to tell what an agent can access, why it has that access, or how to pull it back safely.

This article breaks down the top 4 agent authentication platforms in 2026 for teams already running agents in production who need to choose what to trust.

TL;DR

If you’re skimming, here’s the quick takeaway.

Composio works well when agents need to operate across many tools in production without fragile auth or integration logic
Arcade is a good fit when agent actions are high risk and need tight control at execution time
Merge (Agent Handler) suits environments where governance, auditability, and standardized access matter most
Nango fits teams that already have an agent stack and want OAuth and token handling done cleanly

Each platform reflects a different philosophy about where complexity should live. Teams running agents at scale often lean toward solutions that centralize authentication and reliability rather than managing those concerns themselves.

What Changes When Agents Start Taking Action

Once an agent moves from suggesting actions to actually taking them, everything changes. Reading data is easy. Sending emails, updating records, or triggering workflows requires precise access and clear accountability.

Agents don’t behave like users, and they don’t fit neatly into service-account models either. They run continuously, act on behalf of different users, and touch multiple tools in a single flow, which makes static permission setups hard to maintain.

As agents scale, small shortcuts quickly turn into real problems. Permissions creep, token logic spreads across services, and auditing becomes guesswork. At that point, authentication stops being just a security concern and starts affecting reliability and trust.

How I Evaluated These Platforms

I looked at these platforms through a production lens. The focus was on how well they handle real-world agent behavior as workflows become complex and access starts to sprawl.

That meant looking closely at how each platform manages delegated access, token lifecycles, permission boundaries, and failure scenarios, especially when agents act across multiple tools and users without constant human input.

Just as important was day-to-day usability. How much auth logic do teams still have to own? How easy is it to audit agent actions, rotate access, or roll things back when something goes wrong? Those details matter far more in production than marketing claims.

1. Composio

Composio sits at the intersection of agent execution and authentication, making it a strong option for teams running AI agents against production software tools. Instead of treating integrations and authentication as separate concerns, Composio brings both into a single, agent-first layer.

At its core, the platform removes much of the operational overhead that shows up once agents go live, including OAuth flows, token refresh, permission scopes, retries, and rate limits.

As of 2026, Composio supports 500+ integrations across developer tools, CRMs, communication platforms, productivity software, and internal systems. These integrations are exposed as structured, agent-ready actions rather than raw APIs, which helps keep agent behavior predictable and safer by default.

What sets Composio apart

Composio is intentionally opinionated about how agents should interact with tools, and that opinion shows up in the platform design.

Structured agent actions: Tools provide predefined, typed actions, reducing the need for custom API logic and lowering the risk of unintended behavior.
Managed authentication (AgentAuth): OAuth, token storage, refresh cycles, and scoped permissions are centralized, allowing agents to act on behalf of users without requiring per-integration auth codes.
Production reliability layer: Common failure cases such as timeouts, rate limits, and partial execution are handled by the platform rather than by each agent implementation.
Agent runtime compatibility: Designed to work cleanly with modern agent frameworks and MCP-style setups without tightly coupling agent logic to specific APIs.

Getting Started

Where Composio fits best

Composio works best in environments where agents are expected to operate continuously across multiple tools.

Teams building agent-first products
Internal automation for functions like sales ops, HR ops, engineering, and support
Developers shipping AI features that require reliable access to many third-party tools
Startups and small teams that want to avoid rebuilding integration and authentication infrastructure

For teams that prefer a more guided, non-developer-first experience, Rube, part of the same ecosystem, offers a simpler way to experiment with agent workflows and integrations.

While Composio targets developers who want fine-grained control, Rube lowers the barrier for broader teams to explore agent-driven automation without deep technical setup.

Strengths

Built specifically for AI agents rather than adapted from traditional automation platforms
Strong default handling for authentication and permission boundaries
Reduces duplicated glue code across projects
Scales cleanly as agent workflows become more complex
Enterprise-ready options including SSO, RBAC, SOC 2 Type 2, and on-prem deployments

Limitations

Optimized for technical teams, which may introduce a learning curve for non-developers
Best suited for teams comfortable adopting opinionated abstractions

Pricing overview

Composio follows a usage-driven pricing model that scales with agent activity.

Free tier for local development and experimentation
Paid plans for individual developers and small teams
Usage-based pricing tied to agent action execution volume
Startup credits available for early-stage teams

2. Merge (Agent Handler)

Merge approaches agent authentication from a more enterprise-first angle. Instead of focusing on agent execution itself, it concentrates on controlled access, governance, and standardization across a large number of third-party tools.

For teams running agents inside regulated or multi-team environments, this distinction matters. Merge acts as a unified integration and authorization layer, letting agents interact with external systems through a consistent API while enforcing strict access boundaries behind the scenes.

How Merge handles agent authentication

Merge is designed around the idea that access should be explicit, scoped, and auditable—especially when agents are involved.

User-authorized access: End users connect their tools through Merge’s authorization flow, which agents then act against.
Scoped permissions: Agents only receive access to the specific data and actions allowed by the user and the integration category.
Centralized credential handling: Tokens, refresh logic, and revocation are managed by Merge, not scattered across agent services.
Audit-friendly model: Clear visibility into what data an agent can access and when that access was granted.

This makes Merge particularly appealing in environments where agent access needs to be reviewed, justified, or rolled back cleanly.

Where Merge fits best

Merge works best when control and consistency matter more than flexibility.

Enterprise teams running agents across many SaaS tools
Organizations with strong compliance or security requirements
Internal platforms serving multiple teams or customers
Products that need a unified data and auth layer rather than tool-specific logic

Limitations

Less flexible for highly custom or non-standard agent actions
Not designed as an execution or reliability layer for agent workflows
Can feel restrictive for teams used to direct tool-level control

Pricing overview

Merge typically follows an enterprise-oriented pricing model.

Pricing based on integration categories and usage
Higher starting cost compared to developer-first platforms
Best suited for teams where standardization offsets setup cost

3. Arcade

Arcade takes a different approach from traditional integration platforms. Instead of starting with integrations or APIs, it starts with agent actions and builds authentication and security around how those actions are executed.

This makes Arcade especially relevant for agents that don’t just read or write data, but perform high-impact operations, triggering workflows, modifying systems, or executing commands where mistakes are costly. Authentication in Arcade is tightly coupled to execution and is not treated as a separate concern.

How Arcade handles authentication

Arcade enforces authentication when an agent attempts an action, rather than relying solely on static permissions set up front.

Action-level authorization: Agents request permission at execution time, ensuring access is checked right before an action runs.
Scoped, reusable credentials: Once approved, credentials can be reused safely within defined boundaries.
Execution guardrails: Authentication, validation, and execution happen together, reducing the risk of agents acting outside intended limits.
Built for agent runtimes: Designed to work cleanly with agent frameworks and MCP-style execution models.

Where Arcade fits best

Arcade works best when action safety matters as much as access itself.

Agents performing high-risk or irreversible actions
Systems where execution guarantees are critical
Teams that want tighter control at the moment actions run
Platforms where agent autonomy needs clear boundaries

Limitations

Smaller connector ecosystem compared to integration-first platforms
Less suitable for broad SaaS automation across many tools
Requires teams to align with its execution-first model

Pricing overview

Arcade typically prices around execution and usage.

Usage-based pricing tied to agent actions
Designed to scale with execution volume
Best suited for teams where correctness outweighs breadth

4. Nango

Nango takes a simpler, more focused approach compared to the other platforms on this list. Instead of being an execution layer or a full agent platform, Nango focuses squarely on OAuth and credential management for products that need agents to act on behalf of users.

For teams building SaaS products with embedded agents, this distinction matters. Nango doesn’t try to control how agents behave; it makes sure they have clean, reliable, user-authorized access to third-party tools and then gets out of the way.

How Nango handles agent authentication

Nango is designed to make delegated access boring—in a good way.

User-delegated OAuth flows: Users connect their accounts once, and agents can act within those approved scopes.
Secure token storage and refresh: Tokens are isolated per user and per integration, with refresh handled automatically.
Multi-tenant friendly: Built for products serving many customers, each with their own connected tools.
Flexible integration model: Works well with background agents, workers, and custom orchestration layers.

This makes Nango a strong choice when authentication needs to be reliable and scalable, but agent logic lives elsewhere.

Where Nango fits best

Nango works best when agents are part of a larger product, not the product itself.

SaaS products embedding AI agents for end users
Teams that want full control over agent behavior and orchestration
Multi-tenant systems with many user-connected integrations
Developers who want OAuth handled cleanly without adopting an opinionated agent platform

Limitations

Not an agent execution or reliability layer
Requires teams to handle retries, failures, and guardrails themselves
Less opinionated guidance for agent behavior

Pricing overview

Nango typically prices based on usage and connected accounts.

Free tier for development and testing
Usage-based pricing for production workloads
Scales with the number of users and integrations

Side-by-Side: How These Platforms Compare

All four platforms solve agent authentication, but they are built around very different assumptions about how agents run in production. The real difference is not feature depth, but how much operational complexity each platform absorbs on your behalf.

Platform	What it optimizes for	Works best when	Trade-off
Composio	Agent execution and authentication	Agents operate across many tools with minimal custom glue	More opinionated setup
Merge (Agent Handler)	Control and standardization	Governance and compliance are the main priority	Less flexibility for custom agent behavior
Arcade	Safe action execution	Individual agent actions are high risk	Smaller connector ecosystem
Nango	Clean OAuth infrastructure	Authentication is the only problem you want to outsource	Execution logic stays with the team

The biggest challenges usually appear after agents are live. Tokens expire. APIs hit rate limits. Permissions slowly drift. When authentication, execution, and reliability are handled as separate concerns, teams often end up managing the gaps themselves.

Agent-first platforms that combine authentication, execution safety, and integration reliability tend to hold up better as usage grows. By centralizing these concerns, they reduce the ongoing effort needed to keep agents stable and predictable across many tools.

Teams looking to ship agents that work reliably at scale often gravitate toward platforms that make more decisions upfront, even if that means accepting a more opinionated model.

How to Choose the Right Platform

When agents move into production, the biggest challenge is rarely just authentication. Issues tend to show up together: expired tokens, rate limits, partial failures, permission drift, and brittle integrations that break under real usage.

Teams running agents across many tools often benefit from platforms that treat authentication, execution, and reliability as connected problems
Centralizing these concerns usually reduces ongoing maintenance and surprise failures
Opinionated platforms can feel restrictive at first, but they often scale more smoothly as agent workflows grow
Managing fewer custom glue layers tends to improve long-term stability and developer velocity

Platforms like Composio, built specifically around how agents behave in the real world, generally age better than setups assembled from generic building blocks. When agents are expected to operate continuously and autonomously, that difference becomes noticeable very quickly.

Closing

AI agents in 2026 operate inside real systems and take real actions. Once that happens, authentication becomes part of the core infrastructure that determines how reliable and safe those agents are over time.

The platforms covered in this article approach the problem from different angles, and those differences show up quickly once agents are live in production.

Teams that handle authentication, execution, and reliability together tend to reduce long-term maintenance and avoid fragile glue code.

Platforms designed around agent workflows usually scale more smoothly as agents touch more tools and workflows. Choosing with long-term stability in mind helps teams move faster later, without constantly revisiting core access logic.

9 top AI Search Engine tools in 2026 🤖🔍

Aakash R — Wed, 28 Jan 2026 15:22:37 +0000

AI search engines are everywhere right now, and 2026 is shaping up to be the year they fully change how we search.

Search has moved past simple keywords and long lists of links. AI-powered search engines try to understand what you mean and return clear answers from what is happening right now. As more of these platforms appear, it gets harder to know which ones actually deliver on those promises.

That is where the real challenge comes in. When everything sounds powerful, choosing the right tool becomes confusing.

So the real question is this: which tools give you fresh, accurate answers, help you move faster, and are truly ready for real-world use in 2026?

This article looks at the leading options in AI search today and how well they live up to those claims.

What Makes a Good AI Search Tool in 2026

A good AI search tool in 2026 should feel simple. You ask something, it understands you, and you get a useful answer fast.

It starts with fresh results. A strong tool pulls in new information quickly and keeps answers updated as things change. When results fall behind, the whole experience feels unreliable.

Next comes understanding. You should be able to ask questions naturally, without thinking about perfect wording. The tool needs to catch what you really mean, even if your question is short or casual.

Then there is the answer itself. It should be clear, easy to read, and ready to use without extra work. Speed ties it all together. Long waits break the flow, so search should feel quick and consistent, even when the question is complex.

And finally, there is trust. You should know where the answers come from and have control over what happens to your data.

Categories of AI Search Tools

Before looking at specific tools, it helps to understand the main types of AI search that exist today.

Not every AI search engine is built for the same purpose. Some are made for everyday users, some for developers, and some for heavy data work.

These are the main categories you will see in 2026.

Consumer AI Search Engines: Built for normal users who want quick answers, summaries, and explanations.
Developer-Focused Search APIs: Made for apps, agents, and products that need search built in.
Crawling and Data Extraction Tools: Used to collect, clean, and structure data from websites.
Research and Knowledge Tools: Focused on deep research, long answers, and citations.
Enterprise Search Platforms: Designed for teams that need search across internal documents, files, and systems.

Best AI Search Engine Tools for 2026

Here are the AI search tools that are setting the standard in 2026, each solving search in a different way depending on who it is built for.

1. Exa

Exa is an AI-first search engine built around meaning, not keywords. It uses neural search and embeddings to understand what a query is really asking and then finds content that matches intent, not just popular pages.

Rather than acting like a traditional search engine with ads and ranking tricks, Exa is designed to be a search layer for AI systems. It is widely used inside agents, research tools, and AI products that need high-quality, up-to-date web data.

Exa works mainly through an API. Teams plug it into agents, workflows, or internal tools that need live web search. It supports semantic search, similarity search, domain-restricted search, and structured outputs with citations. This makes it useful for building tools that need reliable sources rather than noisy results.

A big focus of Exa is quality. It tries to avoid spam and low-value pages, aiming to return content that is actually useful.

Who Exa is for: Exa is best for developers, AI builders, and teams that need smart web search inside their products or agent workflows.

Core strengths of Exa

Semantic-first search: Focuses on meaning and intent, not keyword matching.
Strong on complex queries: Handles long, vague, or multi-part questions well.
Built for AI workflows: Designed to plug into agents, RAG systems, and LLM apps.
Higher-quality sources: Filters out a lot of spam and SEO-heavy pages.
Flexible search modes: Supports similarity search, domain filtering, and structured outputs.

Pros

High-quality results: Consistently returns more useful pages than basic scraping or generic search APIs.
Great for AI systems: Works smoothly with agents, RAG pipelines, and LLM-based tools.
Developer-friendly: Clean API, good docs, and easy integration.

Cons

Not user-focused: UI is basic and not meant for casual browsing.
Technical setup needed: Best features require developer work.
Cost at scale: Can become expensive with heavy or enterprise usage.

2. Tavily

Tavily is an AI-focused search tool built mainly for agents and automated workflows. It is designed to give AI systems fast, clean, and structured search results that can be used directly inside reasoning or action-taking pipelines.

Unlike consumer search engines, Tavily is not trying to be a browsing experience. Its main goal is to act as a reliable search layer for AI agents that need fresh web data to think, plan, and act.

Tavily works through an API and is commonly used in agent frameworks, tool-using LLMs, and workflow automation systems. It focuses on speed, simplicity, and consistency rather than fancy interfaces. It supports web search, result summarization, structured outputs, and filtering, making it easy for agents to pull in information and use it without extra cleaning.

Who Tavily is for: Tavily is best for developers and teams building AI agents, automation systems, and tool-using LLM workflows.

Core strengths of Tavily

Agent-first design: Built specifically for AI agents and tool-using models.
Fast search layer: Optimized for quick responses inside agent loops.
Structured outputs: Results are easy for machines to read and use.
Simple integration: Clean API with minimal setup.
Good for planning tasks: Works well when agents need to search, think, and act.

Pros

Great for agent workflows: Fits naturally into multi-step reasoning systems.
Fast and reliable: Low latency for repeated calls.
Easy to use: Simple API and quick setup.

Cons

Not for normal users: No consumer-friendly interface.
Limited browsing features: Focused on function, not exploration.
Depends on external sources: Quality depends on what it can access.

3. Firecrawl

Firecrawl is a crawling and data extraction tool built for turning messy websites into clean, usable data. Instead of acting like a search engine for humans, it focuses on helping machines collect, structure, and understand web content.

It is mainly used when you need to pull data from many pages, clean it up, and feed it into AI systems, databases, or internal tools. Firecrawl handles crawling, parsing, and formatting so teams do not have to build their own scrapers.

Firecrawl works through an API and supports crawling single pages, full websites, or large URL lists. It can return data in formats that are easy for AI models and pipelines to consume. It is commonly used in RAG systems, data pipelines, research tools, and internal search systems where raw web data needs to be cleaned before use.

Who Firecrawl is for: Firecrawl is best for developers and teams that need to collect, clean, and structure web data for AI, analytics, or internal systems.

Core strengths of Firecrawl

Website crawling: Can crawl single pages or entire sites at scale.
Clean extraction: Turns messy HTML into structured, usable data.
AI-friendly output: Formats data so it works well with LLMs and pipelines.
API-first design: Easy to plug into workflows and tools.
Scales well: Handles large crawling jobs reliably.

Pros

Saves time: No need to build and maintain your own scrapers.
Good for RAG: Clean data works well for AI knowledge systems.
Flexible usage: Works for small and large crawling jobs.

Cons

Not a search engine: You need to know what to crawl.
Requires setup: Needs planning for large-scale crawls.
Can get expensive: Heavy crawling increases costs.

4. Perplexity

Perplexity is a conversational AI search engine built for people who want direct answers, not pages of links. You ask a question, and it responds with a clear answer, usually backed by visible sources.

It works like a chat-first search engine. You can ask follow-up questions, go deeper into a topic, or change direction without starting over.

One of Perplexity’s biggest strengths is how it mixes live web search with AI reasoning. It pulls in recent information and turns it into short, readable explanations.

Perplexity also offers different modes, including general search, academic-style research, and focused browsing. Some versions support file uploads and long-context analysis, which makes it useful for working with documents too. It is widely used for learning, writing, research, news tracking, and quick fact-checking.

Who Perplexity is for: Perplexity is best for everyday users, students, writers, researchers, and professionals who want fast answers with clear sources.

Core strengths of Perplexity

Conversational search: Ask naturally and keep the conversation going.
Strong citations: Most answers come with visible sources.
Real-time info: Pulls in recent content from the web.
Multiple modes: Supports general, research, and focused search styles.
Document support: Can work with uploaded files in some plans.

Pros

Very easy to use: No setup, works right away.
Good for learning and writing: Explains topics clearly.
Builds trust: Sources are easy to check.

Cons

Limited control: Not made for deep customization.
Not developer-first: APIs and automation are limited.
Answer depth varies: Some topics get shallow coverage.

5. You search

You search is an AI-powered search engine that focuses on giving users more control over how search works. Instead of a fixed layout, it lets you customize what you see and how results are shown.

It combines AI answers with regular web results. You can get summaries, chat-style responses, and links on the same page. A big part of You.com is privacy. It avoids heavy tracking and does not depend as much on targeted ads. It also offers different “apps” or modes inside search, such as coding help, writing, research, and general chat, all in one place.

Who You.com is for: You.com is best for users who want a customizable, privacy-focused AI search experience.

Core strengths of You search

Customizable layout: Users can choose how results are displayed.
AI plus web results: Mixes chat answers with links.
Privacy focus: Less tracking and fewer ads.
Multiple apps: Writing, coding, research, and chat in one place.
Good for daily use: Works well as a main search engine.

Pros

User control: You decide how search looks and feels.
Privacy-friendly: Less data tracking than big engines.
Versatile: Useful for many everyday tasks.

Cons

AI quality varies: Some answers are weaker than top AI models.
Can feel busy: Too many options for some users.
Less developer focus: Not built mainly for APIs or automation.

6. Serp (SerpAPI and similar tools)

Serp tools are not search engines for humans. They are tools that let developers pull search results from major search engines in a clean, structured way. Instead of scraping pages yourself, you send a query to a Serp API and get back organized data like links, titles, snippets, images, news, and more.

These tools are widely used in analytics, SEO tools, market research, monitoring systems, and AI products that need access to real search results. Serp tools focus on reliability. They handle proxies, captchas, rate limits, and formatting so teams do not have to build and maintain their own scraping systems.

Who Serp is for: Serp tools are best for developers, data teams, and businesses that need large-scale access to search engine results.

Core strengths of Serp tools

Real search engine data: Pulls results from major engines in real time.
Structured output: Returns clean JSON instead of messy HTML.
Scales well: Built for high-volume requests.
Handles scraping issues: Manages proxies, blocks, and captchas.
Good for monitoring: Useful for tracking rankings, trends, and changes.

Pros

Reliable access: No need to build your own scraper.
Works at scale: Handles large workloads smoothly.
Flexible use: Fits analytics, SEO, research, and AI products.

Cons

Not an AI search engine: Just gives raw results, not answers.
Needs processing: You must clean or summarize results yourself.
Can be expensive: High usage increases cost.

7. Brave Search API

Brave Search API is built on Brave’s own independent search index. Unlike many tools that rely on other big search engines, Brave runs its own crawler and index, which gives it more control over data quality and privacy.

The API is mainly used by developers who want real web search results without relying on Google or Bing. It is often used in AI apps, agents, browsers, and privacy-focused products.

Brave Search also supports AI-style answers on top of its index, but its biggest value is giving clean, direct access to raw search data.

Who Brave Search API is for: Brave Search API is best for developers and teams that want independent, privacy-friendly web search inside their products.

Core strengths of Brave Search API

Independent index: Does not depend fully on other search engines.
Privacy-first design: Minimal tracking and data collection.
Good for AI apps: Works well as a search layer for agents and LLM tools.
Structured results: Easy to process programmatically.
Reliable crawling: Continuously updates its own index.

Pros

More control: Not tied to Google or Bing rules.
Privacy-friendly: Strong focus on user data protection.
Good for AI use: Fits well into agent and RAG systems.

Cons

Smaller index: Not as large as big search engines.
Answer quality varies: Depends on index coverage.
Developer-focused: Not built for casual users.

8.Andi Search

Andi is an AI search engine built around a clean, visual, and privacy-first experience. It focuses on presenting answers in a card-style layout with images, links, and short explanations, rather than long text blocks.

It is designed for people who like to explore topics visually. Search results often include summaries, media, and key points arranged in an easy-to-scan format. Andi also puts strong emphasis on privacy. It avoids heavy tracking and keeps the search simple and lightweight.

It works well for general browsing, learning, and discovery, especially when you want a more visual way to explore information.

Who Andi is for: Andi is best for everyday users who want a clean, visual, and privacy-friendly search experience.

Core strengths of Andi

Visual layout: Results appear as cards with text, links, and images.
Privacy-first: Minimal tracking and data collection.
Easy to explore: Good for browsing and discovery.
Simple interface: Clean and uncluttered design.
AI summaries: Short explanations instead of long pages.

Pros

Very clean UI: Easy on the eyes and simple to use.
Good for exploration: Nice for learning and browsing.
Privacy-friendly: Strong stance on user data.

Cons

Not for deep research: Limited control over results.
No strong developer focus: Not built for APIs or automation.
Answer depth varies: Some topics stay surface-level.

9. Parallel AI Search

Parallel AI Search is built for speed and scale. It focuses on running many searches at the same time and combining the results into a single, clean answer. Instead of doing one search step by step, it breaks a query into parts, searches in parallel, and merges everything back together.

This makes it useful for complex questions that need information from many places at once. It is often used in agent systems and research tools where one question turns into many sub-questions.

Parallel AI Search is mostly used through APIs and agent frameworks. It fits well in workflows where an AI needs to explore multiple angles of a problem quickly.

The main idea is simple: faster answers by searching in parallel instead of in sequence.

Who Parallel AI Search is for: Parallel AI Search is best for developers and teams building agents, research systems, or tools that need to explore many sources at the same time.

Core strengths of Parallel AI Search

Parallel querying: Runs many searches at once instead of one by one.
Good for complex questions: Works well when one query needs many sub-queries.
Agent-friendly: Designed to plug into agent and tool-using workflows.
Fast aggregation: Combines results into a single response quickly.
API-first design: Built mainly for programmatic use.

Pros

Very fast for big questions: Parallel search saves time.
Good for agents: Fits multi-step reasoning systems well.
Scales well: Handles many queries at once.

Cons

Not for casual users: No simple consumer interface.
Needs setup: Best used with technical workflows.
Quality depends on sources: Output depends on what it searches.

AI Search Tools Compared

Here is a quick side-by-side look at the most popular AI search tools in 2026, showing how they differ in purpose, features, and who they are best suited for.

Tool	Best For	Live Web Access	Answer Style	Developer / API-First	Privacy Focus	Ease of Use
Exa	AI builders, agents, RAG systems	Yes	Structured, intent-based	Yes	Medium	Medium
Tavily	AI agents & workflows	Yes	API-ready, structured	Yes	Medium	Medium
Firecrawl	Web data crawling & extraction	Yes (via crawl)	Raw structured data	Yes	Low	Medium
Perplexity	Everyday search & research	Yes	Chat & concise with sources	Limited	Medium	High
You.com	Privacy-aware users	Yes	Mixed (chat + links)	Limited	High	High
Serp Tools	Developers needing raw results	Yes	Raw search results	Yes	Depends on setup	Medium
Brave Search API	Privacy-centric products	Yes	Search output only	Yes	High	Medium
Phind	Developers & coders	Yes	Code-oriented, explainer style	Limited	Medium	High
Andi	Visual, everyday users	Yes	Card-style summaries	No	High	High
Parallel AI Search	Agents, research systems, complex queries	Yes	Aggregated from parallel searches	Yes	Medium	Medium

How to Choose the Right AI Search Tool

Picking the right AI search tool depends on what you actually want to do with it. Here are the main things to think about.

Start with your goal: Decide whether you need search for everyday use, learning, research, or building products and agents.
Check how fresh the data is: If you care about news, trends, or fast-changing topics, make sure the tool pulls live or near-real-time information.
Think about control: Some tools are simple and hands-off, while others let you filter sources, tune results, and shape how search works.
Look at privacy: See what data is collected, how long it is stored, and whether you can opt out of tracking.
Plan for cost and scale: A tool that is cheap for light use can get expensive at high volume, so check pricing early.

Closing

AI search in 2026 is about more than finding links. It focuses on giving clear answers, staying current, and saving time.

Some tools are built for everyday users who want quick answers. Others are made for developers, agents, and teams building products. There is no single best option for everyone.

The right choice depends on how you work, what you search for, and how much control you want. Once you are clear on that, picking the right AI search tool becomes much easier.

The tools in this list show where search is heading, and they give a good picture of what modern search looks like in 2026.

Frequently Asked Questions

What makes an AI search engine different from traditional search engines?

AI search engines focus on understanding intent and meaning instead of just matching keywords. Instead of returning a long list of links ranked by ads or SEO tactics, they aim to deliver direct answers, cleaner sources, or structured data. Many are designed to work inside AI systems, agents, or research workflows rather than for casual browsing.

Are these tools meant for regular users or mainly for developers?

It depends on the tool. Products like Perplexity, You.com, and Andi are built for everyday users who want fast answers and easy exploration. Tools like Exa, Tavily, Firecrawl, Serp APIs, and Brave Search API are designed mainly for developers and teams that need search inside AI products, agents, or data pipelines.

Which AI search tool is best for building AI agents or RAG systems?

For agent and RAG workflows, tools like Exa, Tavily, Firecrawl, Parallel AI Search, and Brave Search API are usually the best fit. They offer APIs, structured outputs, and more control over sources, which is important when search results are fed directly into AI models.

Do AI search tools replace Google completely?

Not entirely. AI search tools often replace Google for learning, research, and quick answers. For developers and teams, they can replace scraping or manual search inside products. Traditional search engines still matter for broad discovery and very large indexes, so many people and systems end up using both.

Top AI Integration Platforms for 2026 🤖💥

Aakash R — Tue, 13 Jan 2026 13:42:48 +0000

2026 has just kicked off, and there is already a lot of noise around AI agents. I wanted to look at what they are actually doing in real setups.

For more than a year now, teams have been using agents for everyday tasks like updating CRMs, managing calendars, sending emails, creating GitHub issues, and running workflows across tons of apps.

The part that keeps slowing things down is not the agent logic. It is the glue work around it. Logins, permissions, rate limits, monitoring, and all the tiny connections between tools show up in every project and eat up time.

So I started focusing on the platforms that remove that pain. These integration layers sit between agents and the tools they use, handling the messy parts so builders can spend their time building.

In this guide, I break down the top AI integration platforms of 2026. We will see what they do well, where they struggle, and which one fits best for shipping real agents.

TL;DR

If you just want the short list, here are the platforms worth checking out 👇

Composio: Built for production AI agents with 500+ tools and native MCP.
Arcade.dev: Tight, just-in-time permissions for secure agent actions.
Merge (Agent Handler): Enterprise-grade control over agent actions.
Paragon: Embedded integrations for customer-facing products.
Pipedream: Fast workflows with visual builder and real code.

Each one fits a different use case, so pick based on whether you need speed, control, embedding, or enterprise governance.

What Makes a Great AI Agent Integration Platform in 2026?

By now, one thing is obvious. Not every integration platform works well for agents. Some were built for simple automation, while others are designed around how agents reason, select tools, and navigate tasks.

Platforms designed with agents in mind, especially around MCP and dynamic execution, handle LLM-driven workflows far better than older automation tools. Once you build a real agent, this difference becomes hard to miss.

Here are the main things that separate strong platforms from the rest:

Tool and integration depth: Around 100 to 500+ solid, agent-ready connectors like Slack, GitHub, Gmail, Salesforce, and more.
MCP and agent-native support: Managed MCP servers, smooth use with 20+ agent frameworks, and smart tool routing to avoid context overload.
Security and authentication: OAuth handling, fine-grained permissions, token isolation, and enterprise compliance like SOC 2.
Developer experience: Python and TypeScript SDKs, a CLI for testing, good observability, and fast setup.
Scalability and pricing: Useful free tiers, usage-based pricing as you grow, and enterprise options when needed.
Use case fit: Some platforms focus on pure agent-tool calling, others on embedded product UX, and still others on internal automation.

Top AI Integration Platforms: In-Depth Breakdown

Let us look at how each of the leading platforms performs when you actually try to build and run real agents.

1. Composio

Composio is a developer-first platform that connects AI agents with 500+ apps, APIs, and workflows. It is built for teams who want their agents to move beyond demos and actually work across real tools.

Through a single layer, Composio connects your agent to services like Slack, GitHub, Notion, Gmail, Salesforce, and hundreds more.

In most projects, the hard part is not writing agent logic. The real work sits around integrations: OAuth flows, token refresh, rate limits, retries, error handling, and keeping APIs in sync as they change. Composio takes over all of that and exposes clean, structured tools that agents can call directly.

The platform is designed with agents as the primary users. Each integration is shaped for tool calling, with clear schemas, examples, and updates so LLMs know exactly how to use them and do not break when APIs evolve.

Here is how you can get started:

For teams that want a more plug-and-play experience, Composio also offers Rube. Rube is a universal MCP server that lets agents connect to tools through a single setup and work across clients like Cursor, Claude Desktop, and other MCP-enabled apps, without heavy custom wiring.

Core strengths of Composio

These are the areas where Composio clearly stands out compared to most other platforms:

Large tool ecosystem: 500+ high-quality, agent-ready tools across productivity, dev tools, CRM, support, finance, and more.
Native MCP support: Managed Model Context Protocol servers with universal access through Rube.
Smart tool routing: Automatically selects the right tool and keeps context small.
Strong developer experience: Python and TypeScript SDKs, CLI tools, and fast setup.
Wide framework support: Works with 25+ agent frameworks like LangChain, CrewAI, AutoGen, OpenAI, and Anthropic.
Production-grade security: SOC 2 Type II, least-privilege access, token isolation, and audit trails.
Scalable infrastructure: Serverless setup that handles heavy and spiky workloads.
Tools that evolve: Improve over time based on real agent usage.

Pros

Quick reasons why many choose Composio:

Massive and constantly growing tool ecosystem
Native MCP support and universal access through Rube
Very fast setup and smooth developer experience
Works with most major agent frameworks
Scales well from prototypes to high-volume production systems

2. Merge (with Agent Handler)

Merger is an enterprise-focused platform for governed APIs and secure agent actions. It is known for its unified APIs across categories like HRIS, ATS, CRM, Accounting, Ticketing, and File Storage.

On top of that, Agent Handler extends Merge into the agent space, allowing AI agents to take actions through a controlled, governed layer using Model Context Protocol.

Agent Handler lets agents do things like send messages, create tickets, update records, or trigger background jobs without exposing raw credentials or sensitive data.

Teams can customize tool schemas, names, and descriptions, and even generate connectors by pasting API docs or GitHub links. Authentication, credential storage, rate limits, retries, and error handling are all handled inside the platform.

A big focus for Merge is safety and governance. Agent Handler scans inputs and outputs for sensitive data, applies rules to block or mask content, and provides detailed logs and monitoring. It works with any agent framework and supports multi-tenant setups for customer-facing agents.

Core strengths of Merge with Agent Handler

Deep category coverage: Strong support across regulated business systems like HR, finance, and CRM.
Security-first design: Built-in data scanning, policy controls, audit logs, and role-based access.
Unified API plus agent actions: Use normalized APIs for syncing data and MCP for agent-driven actions.
Flexible tool control: Edit schemas, descriptions, and behaviors to match how agents should use tools.
Enterprise support model: Training, onboarding, and account management for large teams.

Pros

Commonly appreciated aspects of Merge:

Very strong governance and compliance features
Reliable for regulated and enterprise environments
Works with any agent framework
Good fit for customer-facing agents in B2B SaaS

Cons

Areas that may feel limiting:

Higher pricing than developer-first platforms
More setup and configuration
Less focused on large, evolving agent-first tool ecosystems

3. Arcade.dev

Arcade platform focused on just-in-time permissions and community-built tools. It is built around one main idea: agents should only get access to what they need, when they need it. It works as an MCP runtime that lets agents act across tools like Gmail, Slack, GitHub, Salesforce, Google Workspace, and more, while keeping permissions tight and controlled.

Its core design is authentication-first. Agents request scopes only when they are needed. Users approve those requests through a browser flow, and tokens never appear inside the LLM context.

This supports least-privilege access, automatic token refresh, and clear audit trails. It fits well in setups with many users or strict access rules.

Arcade runs as a unified MCP engine. It supports both pre-built tools and custom tools, works with most agent frameworks, and includes features like logs, error handling, and flexible deployment options such as cloud, VPC, or on-prem.

Core strengths

Where Arcade.dev stands out:

Just-in-time permissions: Scopes are requested only when needed, minimizing access.
Strong security model: Tokens stay out of LLM context, with identity provider support and audit logs.
Community-driven tools: Open registry where tools can be shared and extended by the community.
Custom tool support: SDKs for building your own tools and MCP servers.
Flexible deployment: Runs in cloud, private networks, or on-prem.

Pros

What usually works well with Arcade:

Very granular permission control
Good fit for multi-user or enterprise setups
Works with most agent frameworks

Cons

Things to be aware of:

Smaller pre-built catalog than larger platforms
Fewer agent-optimized tools out of the box
Still growing in coverage for edge cases

4. Paragon

Patagon is an embedded integration platform for customer-facing SaaS products. It is designed for B2B SaaS companies that want integrations to feel like part of the product, not an add-on.

It helps ship customer-facing integrations much faster by handling authentication, token refresh, webhooks, rate limits, error handling, monitoring, and scaling behind the scenes, while exposing a white-labeled, native-looking experience inside the product.

Paragon offers 130+ pre-built connectors for tools like Salesforce, HubSpot, Slack, Google Drive, Zendesk, Outlook, and Notion. It also supports custom connectors for any API.

In addition, it provides a workflow builder for automations, a Connect Portal for user onboarding, and ActionKit, a single API for real-time actions, including AI-driven commands and agent tool calls. It supports sync, triggers, and large-scale data movement for use cases like RAG pipelines.

It is built to run at scale, with support for cloud, on-prem, and air-gapped deployments, along with SOC 2 Type II and GDPR compliance.

Core strengths

Where Paragon stands out:

Embedded integration experience: White-labeled, in-app flows with no clunky redirects.
Fast integration delivery: Visual builder and SDKs reduce months of work.
Modern use cases: Supports AI agents, workflows, data sync, and event-driven actions.
Enterprise-ready: Designed for high-volume and regulated environments.
Flexible deployment: Cloud, on-prem, and air-gapped options.

Pros

What works well with Paragon:

Strong customer-facing integration UX
Good support for AI-driven actions and workflows
Reliable at scale

Cons

Limitations to keep in mind:

Custom, enterprise-style pricing
Smaller catalog than very large platforms
More focused on embedded SaaS than pure autonomous agents

5. Pipedream

Platform for fast prototyping with a mix of visual workflows and real code. It is for moving quickly. It lets you connect APIs, databases, and apps in minutes using event-driven workflows, visual builders, and full code steps.

After Workday’s acquisition announcement, it continues to be widely used for automations, internal tools, and quick agent experiments.

It comes with 3,000+ integrated apps and over 10,000 triggers and actions, all with managed authentication. You can build workflows visually or drop in Node.js or Python when logic gets complex. It supports schedules, webhooks, queues, data stores, and private networking.

With MCP support, agents can call thousands of APIs through Pipedream, which makes it useful for tying agents into tools like Slack, Jira, HubSpot, and Asana.

Pipedream also leans into fast AI workflows. You can spin up simple agents, generate code from natural language, and wire everything together quickly. It works well for testing ideas and building internal automations without much setup.

Core strengths

Where Pipedream is strongest:

Very fast prototyping: Build and test workflows or agents in minutes.
Hybrid no-code and code: Visual builder plus full Node.js and Python steps.
Huge integration library: Thousands of apps and triggers ready to use.
Event-driven design: Webhooks, schedules, queues, and real-time triggers.
Good developer tooling: Logs, retries, observability, and serverless execution.

Pros

What usually works well:

Great for experiments and internal tools
Easy to mix visual logic with real code
Large ecosystem of integrations
Generous free tier for getting started

Cons

Limits to keep in mind:

Less focused on deep agent-native design
Fewer agent-optimized tools compared to leaders
Credit-based pricing can grow with heavy usage
More general automation than agent-first platform

Which One Should You Choose?

The right choice depends on what matters most for your setup. A quick way to think about it in early 2026:

Speed to production: Choose a platform built agent-first, with strong tool depth, native agent protocols, and clean SDKs.
Governance and compliance: Pick a platform that offers audit logs, policy controls, role-based access, and strong compliance.
Granular permissions: Look for just-in-time access, task-based scopes, and user-level authorization.
Embedded product experience: Use a platform that supports white-labeled, in-app integration flows.
Rapid experiments: Go with something that supports visual builders, fast setup, and easy custom code.
Full control: Choose open or self-hosted options if ownership and customization matter.

A common pattern is to start with something fast for testing ideas, then move to a more agent-focused platform when building long-running, production systems.

Final Verdict

In 2026, building an agent is easy. Making it work reliably in the real world is the real challenge.

Every platform in this list solves a different problem. Some are built for speed and experiments. Others focus on governance, embedding, or control. The right choice depends on what you are trying to ship.

If your agent needs to take real actions at scale, the integration layer matters as much as the model itself. Choosing the right foundation early saves months of rewrites later.

The agents that win are not the ones that sound smart. They are the ones that actually get work done.

Figma Design to Code: Comparing Figma MCP, OpenAI Codex, and Kombai

Aakash R — Tue, 18 Nov 2025 09:00:52 +0000

Front-end teams now work with more moving parts than ever. Modern applications depend on design systems, component libraries, accessibility constraints, responsive behaviour, routing, state management, and increasingly detailed UI patterns. Every feature requires visual precision and code that aligns with the project’s existing structure.

Design teams mirror this complexity inside Figma. They create layouts using auto-layout, tokens, variants, constraints, and nested components. These files describe what the interface should look like with a high degree of structure, even though they do not describe behaviour or code semantics.

Despite this structure on both sides, the path from Figma to a working application remains manual primarily. Engineers inspect spacing, measure alignment, recreate components, wire interactions, and rewrite everything so it fits a project’s conventions. The workflow is dependable, but slow and repetitive.

This gap has pushed teams to look for ways to automate parts of the handoff. Modern AI models can analyse codebases, understand project patterns, and reason about UI logic.

At the same time, design tools like Figma expose structured information such as hierarchy, constraints, and layout rules. Together, these capabilities create an opportunity to automate more than just static HTML scaffolds.

To explore how far current tools have progressed, this article evaluates three approaches on the same design and the same stack:

Figma MCP: It extracts structured visual information directly from the Figma file and provides that data to a coding agent for generation.
Codex CLI: It generates UI code by reasoning within an existing repository, understanding files, dependencies, and established conventions.
Kombai: It interprets both the Figma design and the surrounding codebase together, producing context-aware components that align with real project structure.

The goal is not to generate a screenshot-accurate mockup, but to understand whether these tools can produce code that is reusable, consistent with the framework, and ready for production.

Why Turning Designs into Code Remains a Hard Problem

Figma describes the visual structure of an interface, while a codebase defines how that interface should function. Both represent the same UI, but they capture very different types of information. Figma focuses on layout and appearance, whereas production code must express behaviour, data flow, interaction, and the architectural patterns of the project.

This gap becomes clearer when looking at the layout. Auto Layout provides order and alignment inside Figma, but it does not map directly to Flexbox, CSS Grid, or responsive breakpoints. A layout that appears precise inside Figma often behaves differently once rendered in a browser. Many generators try to preserve the artboard by hard-coding fixed values. The output looks correct at first, but it fails when the text grows longer, the content becomes dynamic, or the viewport changes.

The difficulties increase when the generated UI meets a real codebase. Most projects already contain reusable components, naming conventions, design tokens, routing structures, and state management patterns. Generic generators cannot infer these rules. They often recreate components the team already has, producing code that runs but does not align with the existing system.

Behaviour adds another layer of complexity. Figma shows static visual states but does not encode their logic. Icons may imply dropdowns, date pickers, or navigation, yet none of this behaviour is present in the design file.

These gaps explain why most tools struggle to produce code that is ready for real applications.

Benchmark Design and Evaluation Environment

To compare the three tools fairly, a single benchmark design was used across all evaluations. The homepage of a publicly available Figma template served as the test case. It includes navigation, a hero section, property cards, search and filtering elements, and a detailed footer, offering enough layout variety and visual complexity to assess how each tool handles a realistic interface rather than a minimal example.

Figma Access: This evaluation used the Desktop MCP server, which requires the Figma Desktop application with Dev Mode enabled. The Desktop server provides full access to the design structure and metadata. (Figma also offers a Remote server that does not require Dev Mode, but it was not used here.)

Local Setup: Codex CLI and Kombai both run locally. Their setup processes differ, and the required steps are explained separately for each tool.

Evaluation Focus: The goal is to observe whether each system can generate code that:

Uses a logical component structure
Separates data from UI
Handles assets cleanly
Produces working interactions
Matches the visual design accurately

Running all three tools against the same design ensures that differences in output reflect the tools’ capabilities rather than variations in input.

Figma MCP

Figma MCP brings an interesting approach to turning designs into code. Instead of exporting images or relying on visual screenshots, MCP exposes the actual structure of a Figma file, frames, auto-layout rules, typography, spacing, and component metadata. An external agent can request this information and generate code based on what it learns from the design.

In theory, this should produce code that is more accurate than a pixel-based interpretation. Since the agent receives hierarchy and layout data directly from Figma, it has the information needed to understand how elements are positioned and how they behave.

Setup

For this evaluation, Figma MCP was accessed through the local server exposed by the Figma Desktop app with Dev Mode enabled. This environment gives external tools full access to the file’s structural data, including hierarchy, auto-layout behaviour, constraints, spacing, typography, and component metadata.
Since the server streams structured information rather than screenshots, the connected agent receives an accurate representation of how the interface is organised inside Figma.
Cursor was used as the MCP-compatible client during this test, but any IDE or agent that supports MCP could be used in the same way. The local server URL was added to the client’s configuration so it could request design metadata directly from the active file.
Once connected, selecting a frame or sharing its link allowed the agent to fetch its underlying properties and generate React code based on the actual layout and component structure defined in Figma, without requiring exports or manual inspection.

Outcome

Here is a screenshot of the output (see the full site at https://estatein-website-bice.vercel.app/).

The MCP-based generation produced a functional React page, and the overall page structure closely resembled the original homepage hierarchy. However, many sections were rebuilt in a simplified form. Icons were replaced with solid placeholder shapes, background images were dropped, and card components lost their visual details, such as shadows, rounded corners, and overlays.

Sections that relied on nested Auto Layouts in Figma were reconstructed using broad container divs with fixed spacing, which resulted in uneven gaps and typography scales that did not match the Figma values.

From a code perspective, MCP generated a single large component for most of the page instead of splitting it into smaller reusable units. The output did not separate data from UI, and mock values were embedded directly inside JSX. Asset handling was incomplete because only a small subset of vector information was extracted, and no images or SVGs appeared in the repository.

The page was responsive, but the responsiveness came from generic flex layouts rather than breakpoints modeled after the design.

Interactive elements such as buttons and inputs were present as basic HTML, but no behaviour was inferred from the design. Most importantly, the visual accuracy was low: spacing, colors, and component proportions differed significantly from the Figma file, so the result served more as a rough scaffold than a close match to the original design.

Codex CLI

Codex CLI works as a local AI coding agent. Instead of relying on a design-specific API like Figma MCP, Codex operates directly inside the developer’s workspace. It can create new files, install dependencies, modify existing code, run shell commands, and build projects through natural language instructions.

The CLI supports common frontend stacks such as React and Next.js, which makes it useful for quickly scaffolding projects from a visual reference or developer prompt.

Setup

Codex CLI was evaluated as a local AI coding agent that works directly inside the developer’s workspace. It requires an active OpenAI plan (Plus, Pro, Business, Edu, or Enterprise) and operates by analyzing the project folder, understanding existing files, and generating new code through natural language instructions.
Once installed and initialized in a fresh directory, Codex could scaffold a full React or Next.js project, install packages, and run commands without needing any additional configuration.
Because Codex cannot read Figma files or query design metadata, the homepage design used in this comparison was exported as a PNG and added to the project. The agent was instructed to recreate the UI based on the screenshot and generate reusable components.
Codex produced the initial layout, set up the project structure, and launched the local development server. Any refinements, such as adjusting spacing, splitting components, or revising styles, were done through follow-up prompts, which Codex translated into file edits.

Outcome

Here is a screenshot of the output (see the full site at https://codex-cli-1fqs.vercel.app/).

Codex reproduced the overall page layout from the screenshot and generated a functional Next.js project. The structure of the hero section, navigation, cards, and footer followed the exact broad ordering as the Figma file, and the page behaved responsively due to Codex relying heavily on Tailwind’s default flex and grid utilities.

However, the generated UI diverged noticeably from the real design. Spacing between elements was uneven, column widths did not follow the grid proportions from Figma, and the typography scale drifted from the intended hierarchy, especially in headings and card titles.

Because Codex works from an exported PNG, none of the original assets appeared in the output. All icons, property images, background artwork, and decorative UI elements were omitted, leading to multiple sections looking empty or structurally incomplete. For example, the property cards contained text but lacked images; the navigation relied entirely on plain text; and the hero section was rendered without its main visual anchor.

The underlying code showed partial componentisation. Some sections were placed in their own files, while others remained within the page component, and the layout logic was tightly coupled to JSX rather than abstracted into reusable components. Data and UI were mixed in the duplicate files, and no interaction logic was generated because Codex cannot infer behaviour from a static screenshot.

The final output resembled the design only at a high level. It served as a usable draft for layout scaffolding. Still, it would require significant manual work to restore assets, correct grids, refine typography, and rebuild components to align closely with the original Figma design.

Kombai

Kombai is built specifically for frontend development, and Figma-to-code is one part of what it supports. It is designed to generate production-ready UI across 30+ modern frontend libraries, including React, TypeScript, Next.js, Vue, Svelte, Mantine, MUI, and more.

When reading a Figma design, Kombai does not treat it as a screenshot or a raw JSON export. Instead, it interprets the layout, structure, and UI patterns the way a frontend engineer would. It identifies elements such as grids, cards, navigation bars, inputs, buttons, and form layouts and converts them into clean, reusable components that fit the conventions of the selected framework.

A key difference from MCP and Codex is Kombai’s understanding of project context. It can generate code for a new repository or integrate directly into an existing one, reusing components, hooks, styles, and design tokens already present. This allows the generated output to align with the project’s architecture, making it suitable for real production use rather than serving as a draft that requires extensive cleanup.

Setup

Kombai had the simplest setup of all three tools. There were no servers to configure and no API tokens to manage. The extension was installed from the IDE’s marketplace and appears as a panel in supported editors such as VS Code, Cursor, Windsurf, and Trae.
After signing in, the extension prompts the user to connect their Figma account through the standard Figma login flow. Once connected, Kombai can read design files directly without requiring exported PNGs or manually prepared assets.
To bring the homepage design into Kombai, the Figma frame link was copied and added inside the extension. After the selection was confirmed, Kombai analysed the design and completed a planning stage to understand the layout, components, and UI patterns. Code generation typically takes a few minutes.
When it finishes, Kombai writes React components, pages, assets, and routing files directly into the repository. The project runs immediately inside the IDE, allowing refinement or extension without any additional setup.

Outcome

Here is a screenshot of the output (see the full site at https://kombai-agent-roan.vercel.app/).

Kombai produced a React project that closely followed the original Figma design. The layout matched the intended grid, spacing rules, and visual hierarchy, and the project ran immediately without missing dependencies or broken imports. Instead of generating a large monolithic page, Kombai split the UI into reusable React components with clear props and separated data structures.

All icons, images, and background graphics were extracted directly from the Figma file and placed in the correct asset folders. The generated components referenced these assets cleanly through typed imports.

The visual fidelity was high. Typography scales, color values, and spacing patterns aligned with the original design rather than an approximated interpretation. Interactive elements such as buttons, navigation links, and cards behaved as functioning UI components. The output required only one correction during testing: a minor logo color mismatch.

Post-generation debugging:

After code generation, the project was opened in Kombai Browser, which allows visual inspection and element-level refinement. The logo color issue was fixed by selecting the logo via the Reference Selection tool and prompting Kombai to apply the original Figma color.
The system regenerated only the relevant part of the code within a few seconds, and the correction applied cleanly without manual edits. This level of targeted debugging made refinement faster and more controlled compared to re-prompting or editing files manually.

Evaluation of the tools

All three tools received the same Figma homepage and the same prompt: generate a working React version of the design with no manual help.

Figma MCP generated a functional page but missed many visual details, resulting in a scaffold rather than an accurate translation of the design.
Codex CLI recreated the general layout from the screenshot, but spacing, typography, and all major assets were off, making the output only loosely aligned with the original.
Kombai produced the closest and most complete match. It accurately replicated layout, styling, components, and assets, and ran without fixes, with visual debugging available for targeted refinements.

Here is the comparison table:

Kombai was the only tool that reproduced the homepage almost exactly as seen in Figma. The layout, spacing, type scale, cards, navigation, and assets came through correctly, and the project ran immediately with no dependency issues or missing files.

The generated code followed a clean structure. Pages were split into reusable components, styling was organized, and assets were extracted instead of replaced with placeholders. Buttons, dropdowns and inputs worked as real interactive elements, not just static UI.

Post-generation refinement made a noticeable difference. After the first run, the project could be opened inside Kombai Browser, which shows a live preview of the generated UI. If something looked off, the element could be selected visually and Kombai regenerated that part of the code. In this test, the logo appeared white instead of dark. Selecting it in the browser and asking Kombai to apply the original Figma color fixed the issue in seconds.

Kombai also works inside existing codebases and reuses components already present in a repo. This made it the only tool that produced a high-fidelity result and integrated cleanly into real projects.

Conclusion

This comparison highlights that Figma MCP, Codex CLI, and Kombai each approach design-to-code generation differently. MCP provides reliable access to structured Figma metadata, and Codex performs well as a general-purpose coding assistant inside a project. Both can bootstrap a layout, but neither maintains enough visual or structural accuracy to move beyond an early scaffold.

Their limitations become clearer when applied to a real design. MCP preserves hierarchy but loses assets and key visual details. Codex reconstructs the layout from a screenshot but cannot recover typography, spacing, or images. In both cases, substantial manual work is required before the output resembles the intended design or matches a project’s coding patterns.

Kombai closes this gap by interpreting both the Figma file and the codebase. It generates reusable components, extracts assets correctly, and recreates the layout with high fidelity. For teams looking for production-ready design-to-code automation, it delivered the most complete result in this evaluation.

Framelink Figma MCP vs Kombai: Which is Best for Figma-to-Code Automation?

Aakash R — Tue, 11 Nov 2025 06:35:32 +0000

Turning a Figma design into clean, working frontend code always takes more effort than it should.
You open the file, and suddenly you’re naming layers, adjusting spacing, rewriting components, and fixing weird flex behaviors.

Even when the design system is solid, the translation from design to code is never automatic. It’s still a lot of manual work. That’s the space tools like Figma MCP, and Kombai are trying to fix.

Both aim to bridge the design-to-code gap but in very different ways. Figma MCP gives AI models access to design context inside Figma, so they can interpret layouts and structure.

Kombai focuses on reading those same designs and generating production-ready React or Tailwind code that fits into your existing project.

This post breaks down how both actually work in practice, how they read designs, how the code turns out, and how each fits into a real frontend workflow.

Understanding Figma MCP

When I started exploring Figma MCP, I expected a design-to-code tool. It is not that. MCP stands for Model Context Protocol. It allows an AI model to read a Figma file in a structured format instead of just looking at an image.

Imagine a button design inside Figma. It has a rectangle, a text label, and an auto layout rule. Normally, an AI tool would only see that as pixels. With MCP, the model can access detailed data like the layer type, font style, color value, and position.

This data is shared in a format similar to JSON, which makes it easier for a model to understand what the design actually contains.

MCP itself does not generate any code. It only passes this design information to a connected model. The model decides what to do with it. It can produce React code, check for design consistency, or even document components.

This setup gives us, the developers, the flexibility. You can connect your own AI model, define how the design should be processed, and customize the output. The trade-off is effort. You have to handle the logic, the code generation, and how the output fits into your project. MCP gives you the base, but you have to build on top of it.

How Kombai Interprets and Generates Code

So what is Kombai anyway?

After spending some time with it, I see Kombai as an AI assistant that understands both design and frontend development. It reads a Figma design, analyses its structure, and writes usable code that actually fits into a real project.

When I imported my first design, I expected something basic. What I got was well-structured React code that reflected how the layout was organised in Figma. Kombai detected common patterns like cards, buttons, and navigation bars, then turned them into reusable components with clear naming and proper props.

The output looked like something a developer would write. It used Tailwind classes consistently, kept the hierarchy clean, and avoided unnecessary nesting. I could drop the component into my project, and it worked without major fixes.

Kombai does more than just read Figma files. It adapts to your existing setup. It can look at your project, understand how components are named, and follow your conventions in the generated code. That makes the integration feel smooth instead of disconnected.

The workflow is simple. You select a frame, Kombai analyses it, generates code, and gives you a preview in its sandbox. It also checks for TypeScript and lint errors automatically, which saves time during review.

It still needs developer oversight, especially for responsive layouts or complex logic, but it gets most of the structure right. The best part is that the code it generates feels natural. You can read it, edit it, and extend it without having to clean up a mess.

Prerequisites

Before running the comparison, I used a template that has a hero section, multiple content cards, and call-to-action elements. It’s a balanced layout with enough variety to test how both tools interpret structure, typography, and color handling.

I used the same sections of the design for both tools to keep the test consistent.

For Figma MCP, I connected it to a model that could read design context from Figma and generate React code.

For Kombai, I used the Kombai Cursor extension inside my editor to bring in the same design and generate React and Tailwind components.

Example Comparison

Ok, enough with the setup. It’s time for a proper showdown.

I ran both tools on the entire website design and built out every section to see how far they could go without manual fixes.

Execution with Figma MCP

Here’s how I set up and ran Figma MCP using the Framelink MCP server inside Cursor.

1. Generate a Figma Personal Access Token

Open Figma in your browser.
Click your profile icon → go to Settings.
Scroll down to Security → find Personal access tokens.
Click Generate new token.
Copy the generated token and keep it safe; you will need it for configuration.

2. Configure the MCP Server in the Cursor

Open Cursor.
Go to Settings → Tools → MCP.
Add a New MCP Server entry.
Paste one of the following JSON configurations, depending on your operating system.
Replace YOUR-KEY with your Figma personal access token.

MacOS / Linux

{
  "mcpServers": {
    "Framelink MCP for Figma": {
      "command": "npx",
      "args": ["-y", "figma-developer-mcp", "--figma-api-key=YOUR-KEY", "--stdio"]
    }
  }
}

Windows

{
  "mcpServers": {
    "Framelink MCP for Figma": {
      "command": "cmd",
      "args": ["/c", "npx", "-y", "figma-developer-mcp", "--figma-api-key=YOUR-KEY", "--stdio"]
    }
  }
}

3. Restart Cursor

After saving the configuration, close and reopen the Cursor to ensure the MCP server is loaded correctly.

4. Verify the MCP Connection

Open Agent Mode in Cursor.
In Figma, select the frame you want to convert.
Right-click the frame → Copy/Paste As → Copy link to selection.
Paste the frame link into Agent Mode.
If everything is connected correctly, the Cursor should respond to the Figma context or show a confirmation that the frame was read successfully.

5. Generate the Code

Once the connection is verified:

Stay in Agent Mode.
Paste the Figma frame link again.
Ask the agent to generate React + Tailwind code for that frame.
Review the generated output. You can save, modify, or export it directly from Cursor.

Here is the outcome:

Execution with Kombai

Kombai was much easier to set up and run compared to MCP. The process is straightforward and doesn’t require any manual configuration.

1. Install Kombai

If this is your first time using Kombai, go to kombai.com and download the extension that matches your editor.
Kombai supports VS Code, Cursor, Windsurf, and Trae.

Once downloaded, install the extension in your editor.

2. Connect Your Figma Account

Open the Kombai extension inside your editor.
Log in to your Kombai account (or create one if needed).
You’ll be prompted to connect your Figma account.
Authenticate using your Figma credentials and allow access.

Once this is done, your Figma is now linked to Kombai.

3. Import the Figma Design

Open the Kombai extension inside your IDE.
In the chat bar, click the Figma option.
Go to Figma and select the frame you want to convert.
Right-click → Copy/Paste As → Copy link to selection.
Paste the copied Figma link into Kombai.

Kombai will automatically start analyzing the design.

4. Generate and View the Output

After you paste the link, Kombai begins converting the design into code.
This usually takes a few seconds to a few minutes, depending on the design’s complexity.

Here is the outcome:

Once done, you can view the generated output inside Kombai Browser (Beta).
It shows a live preview of your design converted into code. You can explore the output and verify how close it is to the original Figma layout.

5. Debug or Refine the Output

If something doesn’t look right, Kombai lets you debug directly in its browser.
You can use the Kombai Agent to fix specific elements:

Attach an image showing what’s wrong.
Select the exact UI element using the Reference Selection tool.
Kombai will adjust or regenerate the code for that part of the design.

It’s a fast way to correct spacing, alignment, or color mismatches without manually editing the generated files.

Outcome Comparison

Both tools were tested on the same Website Template inside Cursor. For Figma MCP, I used the Framelink MCP server setup, which required manual configuration with an API token. For Kombai, I used the Kombai Cursor extension, which was much easier to set up, install, connect to Figma, and start converting designs right away.

The Figma MCP output captured the structure but needed work before it was usable. The layout was correct, yet the details were off. Spacing and alignment required manual adjustments, and image rendering wasn’t consistent. The result looked more like a functional wireframe than a finished UI.

The Kombai output came out much closer to the actual design. It maintained consistent spacing, colors, and typography, and the component hierarchy looked natural. Images displayed correctly, and the generated code was organized enough to use directly with small tweaks.

In short, MCP handled the structure well, but Kombai’s setup and output both felt more practical for real frontend work.

How Kombai Closed the Design-to-Code Gap

After spending time with both tools, the real difference wasn’t just in setup or output. It was in how effortlessly Kombai fit into a real development workflow.

1. Simple Setup, Faster Start

Setting up MCP took time, like generating tokens, configuring the server, and checking connections. Kombai was ready within minutes. Install the extension, connect Figma once, and you’re good to go. No server setup or manual configuration.

2. Clean, Reusable Code

Kombai produced readable, reusable components that followed modern frontend patterns. Props were cleanly defined, Tailwind classes were appropriately used, and the structure matched real-world React projects. MCP’s output was functional but needed refactoring to reach the same quality.

3. Adapts to Existing Codebases

Kombai fits into your existing project without breaking conventions. It recognizes file structure, component naming, and styling patterns. The generated code blends in naturally; no need to rewrite or reorganize to make it work with your setup.

4. Flexible Tech Stack Options

Kombai is not tied to a single framework. You can choose or edit the tech stack before generation. It supports React, Tailwind, and other frontend combinations, and can align with your preferred libraries or design systems.

5. Design Fidelity and Feedback

The output consistently matched the original Figma design, and spacing, typography, and color accuracy stayed intact. The Kombai Browser preview also made debugging easy. You could immediately see issues, highlight an element, and fix them without leaving the environment.

Conclusion/Summary

Working with both tools made it clear that they approach the same problem differently. Figma MCP is a general protocol that allows AI models to access design data, but it is not built specifically for production-level code generation. It gives developers flexibility, but most of the structure and logic still need to be handled manually.

Kombai, on the other hand, is built around the design-to-code workflow as one of its core capabilities. It understands design structure, adapts to existing codebases, and produces clean, readable components that fit into real projects. The setup is simple, the feedback loop is fast, and the output is close to what developers would write themselves.

If you want to see how an AI tool can work with your code instead of just generating it, try Kombai in your editor and run it on your own designs.

How to use Gmail MCP with OpenAI Agent Builder ✉️ 🚀

Aakash R — Fri, 31 Oct 2025 07:31:31 +0000

At DevDay on October 6, 2025, OpenAI launched AgentKit, a toolkit that makes it easier to build AI agents that can use real apps and data. The new Agent Builder lets you connect these agents to external tools via Model Context Protocols (MCPs).

Gmail is a good starting point because it is simple to connect and instantly useful. Your inbox already holds clear tasks, messages to read, replies to send, and updates to follow. That makes it a natural place to see how an agent can take action instead of just answering questions.

We are going to connect Gmail to Agent Builder and see what happens when your agent learns to handle email.

Let’s goooo!

What is Agent Builder?

Agent Builder is part of OpenAI’s new AgentKit. It gives you a place to build AI agents that can actually get work done. Everything happens in one environment, from planning your agent’s steps to testing how it performs.

The interface feels familiar if you have used any workflow or automation tool before. You can drag and connect nodes to design how your agent thinks and acts. Each node represents an action, a decision, or a connection to a tool. You see the full logic without digging through code.

Here is why developers and beginners like it:

You can connect external apps with a few clicks.
You can add logic and conditions visually instead of writing long scripts.
You can test your agent’s actions in real time and see results instantly.
You can set limits and control what your agent can do.

Agent Builder removes much of the setup work and lets you focus on what your agent should actually accomplish. It is simple to start with and powerful enough to take it further as your ideas grow.

What is Rube MCP?

Rube MCP is a connector that links OpenAI’s Agent Builder with the apps you use every day. It acts as a single bridge for your agent to access 100s of tools like Gmail, Slack, or Notion.

It follows the same Model Context Protocol standard as Agent Builder, which means everything fits together smoothly. You don’t have to manage multiple APIs or complex authentication. Rube handles that in the background.

Once connected, your agent can use real data, send messages, and complete actions in other apps, all from one place.

Prerequisites

Before you connect Gmail to Agent Builder, make sure a few things are ready.

You need access to the OpenAI Agent Builder. Log in with an account that can create or edit agents.
You need a Google account with Gmail enabled. This is the account your agent will connect to when sending or reading emails.
You will also need the Rube MCP set up as a connector. It acts as the link between Gmail and Agent Builder. Once it’s active, Gmail will appear as an available MCP inside the Builder.

That is all you need. With these three pieces in place, you are ready to connect Gmail and see your agent in action.

How to Add Gmail MCP in Agent Builder

To connect Gmail, you’ll use Rube MCP as the bridge between OpenAI’s Agent Builder and Gmail. Rube takes care of the backend setup so your agent can talk to Gmail securely and without custom code.

Here’s how to set it up from start to finish.

Step 1: Get Your Rube MCP Endpoint

Before connecting Gmail inside Agent Builder, you first need to set it up in Rube. This gives you the endpoint and token your agent will use to connect.

Follow these steps:

Go to Rube’s website and sign in or create an account.
Open your Dashboard.
From the left menu, select Apps and search for Gmail.

Select the required scopes. All scopes are selected by default, so if you’re fine with full access, leave them as they are.
Complete the authentication process and approve the required permissions.
Once authenticated, you will see a notification confirming that Gmail is connected.
Click the Install button and choose the Agent Builder option.

Scroll down and copy your MCP Endpoint.
Generate a Token as well and save both; you will need them in the next step.

This setup gives you the secure connection details that allow OpenAI’s Agent Builder to talk to Gmail through Rube.

Step 2: Open Agent Builder

Go to the OpenAI Agent Builder.
Create a new agent or open an existing one.
You’ll land on the main canvas, where you can design your agent’s workflow and connect tools.

Step 3: Add the Rube MCP Server

There are two ways to add MCP serve, you can use the mcp noce to do that or you can go to agnet node and open settings > Tools > MCP server

In that box, click the Add button to connect a new app.
Choose the + Server option.
Paste the endpoint you copied from Rube in the previous step.
Add a label, for example Rube.
Enter the Token you generated in Rube.
Click Connect.

- You will see a MCP is now connected with the agent successfully under tools.

Build a Smart Gmail Workflow in Agent Builder

This workflow checks your Gmail inbox for unread emails using Rube MCP, and if any are found, it sends you a short summary email.

If there are no unread messages, the workflow simply ends.

It’s perfect for staying on top of your inbox without constantly checking Gmail yourself.

Step 1: Set Up the Main Flow

Open your agent in Agent Builder.
On the main canvas, you should already see the Start Node.
Add three more nodes:
- Guardrails Node
- Agent Node (for Gmail actions)
- End Node

Your layout should look like this:

Start → Guardrails → Gmail Agent → End

This simple layout ensures that:

The Guardrails node checks your Gmail connection first.
The Gmail Agent node only runs if everything is fine.
The End Node closes the workflow cleanly.

Step 2: Configure the Start Node

This node begins your workflow.
It simply signals where the workflow begins when you click Run (or if you later connect this agent to an external trigger, such as a webhook or another system).

Step 3: Add the Guardrails Node

Add a Guardrails Node and connect it after the Start Node.
In the Guardrails settings, enable the “Jailbreak” option.

This makes sure your workflow stays secure by blocking any malicious or injected instructions that could try to override your agent’s rules or access Gmail in unsafe ways.

Enabling Jailbreak keeps your agent’s behavior controlled and prevents prompt manipulation before continuing to the next step.

Connect Pass → Gmail Agent
Connect Fail → End

✅ Why this matters:

It prevents workflow errors when your Gmail connection token expires or if Rube MCP becomes unreachable.

Step 4: Add the Gmail Agent Node

Add a new Agent Node and name it Gmail Agent (Unread Email Notifier).
Connect it from Guardrails → Pass.

Now, connect your Gmail MCP:

In the left sidebar, click Tools → MCP.
Click Add → choose + Server.
Paste your Rube MCP Endpoint and Token (from your Rube dashboard).
Click Connect.
Once connected, Gmail will appear as an available MCP.

Now that Gmail is active, go to the Prompt Tab and enter this prompt:

Use the connected Gmail MCP to fetch all unread emails from the inbox.

If there are any unread emails:
- Show each sender’s name and email.
- Include the subject line.
- Count how many unread messages exist.
- Summarize each email briefly (one or two lines per email).
- Send me an email using Gmail MCP with:
  - Subject: "Unread Emails Summary"
  - Body: Include the total number of unread messages and their summaries.

If there are no unread emails:
- Do nothing and end the workflow.

✅ What this does:

The agent first checks your inbox for unread messages.
If unread messages exist, it summarizes them and emails the summary back to you automatically.
If there are none, it ends quietly.

Step 5: Add the End Node

Add an End Node to your canvas.
Connect:
- Guardrails (Fail) → End

This ensures your workflow always terminates gracefully, whether it runs successfully or fails a safety check.

Step 6: Review the Full Layout

Here’s your completed workflow structure:

Start
   ↓
Guardrails (Check Gmail Connection)
   ├── Pass → Gmail Agent (Unread Email Notifier)
   └── Fail → End

Step 7: Test the Workflow

Click Preview in the top-right corner.
Select Run to execute the workflow.
Observe the output:
- If Gmail MCP is connected, the Guardrails node passes.
- The agent fetches unread emails.
- You receive an email summary if new unread messages exist.

If it doesn’t run correctly:

Double-check that the Rube MCP endpoint and token are correct.
Make sure Gmail permissions were granted when connecting through Rube.
Retry with a manual trigger.

✅ Output

When unread emails exist, you’ll get an email like this:

If there are no unread emails, the workflow ends without sending anything.

🧠 Optional Enhancement

You can make the workflow smarter by adding a Condition Node after the Gmail Agent:

If output contains "urgent" → send a Slack notification.
Else → End.

This way, your agent can automatically alert you when something important hits your inbox.

Real-World Use Cases

Once Gmail is connected through Rube MCP, your agent can do more than check messages. It can handle repetitive work, organize your inbox, and even help with communication tasks. Here are a few ways to put it to use.

1. Daily Inbox Summary

This workflow checks your inbox every morning and pulls unread emails from the past 24 hours. The agent then summarizes them into a short digest and sends it to your Gmail or Slack.

It’s a simple setup that saves you from scanning through dozens of emails before you even start your day. Instead, you get a clear summary of what actually matters.

2. Turning Emails into Tasks

By linking Gmail with a task manager like Notion, ClickUp, or Asana through Rube MCP, your agent can convert email content into tasks. It scans new emails for phrases like “Please finish this by Monday” or “Can you update this report?” and creates a task automatically.

This keeps your inbox cleaner and ensures that every request becomes something actionable in your workflow.

3. Meeting Prep Assistant

Your agent can read upcoming meeting invites and gather context from related email threads. A few minutes before the meeting, it sends you a short summary with the topic, participants, and key discussion points.

It’s a small automation that helps you stay organized and prepared without digging through old threads.

4. Multi-App Workflows

Rube MCP also lets your agent combine Gmail with other apps. You can create workflows that send email updates to Slack, save attachments to Google Drive, or update project notes in Notion.

This is where things get powerful. Once Gmail is connected, it becomes part of a larger network of tools your agent can use.

Final Verdict

Knowing how each node works is useful, but what really matters is how they connect. The power of Agent Builder comes from understanding how data moves between nodes and how to shape that flow to match your goal.

Once you get that part right, building smart, reliable agents becomes much easier. You’ll know which nodes to use, how to pass data cleanly, and how to turn a simple setup into something that runs well every day.

Agent Builder makes this process smooth for both beginners and developers. It lets you build and test complete workflows without spending time on setup. And with Rube MCP, adding tools like Gmail feels fast and straightforward.

If you’re curious, open Agent Builder, link Rube MCP, and build your first workflow. You’ll pick things up faster than you expect.

Frequently Asked Questions (FAQs)

1. Do I need to know how to code to use Agent Builder?

No! That’s one of the best parts of Agent Builder. You can design logic visually using nodes, just drag, connect, and adjust prompts. No coding is required unless you want to extend functionality or create custom connectors later.

2. Is Rube MCP safe to use with my Gmail account?

Yes. Rube MCP follows Google’s secure OAuth authentication flow, so your login and permissions stay protected. It only accesses the scopes you approve during setup, and you can revoke access anytime from your Google Account’s security settings.

3. Can I connect other apps besides Gmail?

Absolutely. Rube MCP supports a growing list of apps like Slack, Notion, Google Drive, ClickUp, and more. Once your Gmail workflow is running, you can easily expand it, for example, forwarding summaries to Slack or saving attachments to Drive.

4. Can I keep using Rube MCP as I build more complex agents?

Yes. Rube MCP is designed to scale with you. You can start small like connecting Gmail for simple summaries and later expand into multi-app workflows without changing your setup. The same Rube connection can handle multiple agents and tools, which means you can reuse your integrations instead of rebuilding them every time.

13 MCP Servers Every Developer Should Use 💻

Aakash R — Tue, 09 Sep 2025 05:46:22 +0000

Model Context Protocol, or MCP, is one of the most exciting things happening in developer tools right now. If you want to maximise the benefits of AI in your workflow, now is the ideal time to start exploring it. There is a significant amount of new work happening in this space, and developers from diverse backgrounds are beginning to adopt it.

But figuring out which MCP servers are worth your time can be tricky.

I have compiled a list of MCP servers that every developer should be aware of.

1. Rube MCP: Universal MCP server for all your apps

When you start using MCP servers, the first hurdle is setup. Each server like GitHub, Slack, Supabase, Postman, and so on, needs its own configuration. That’s fine for one or two, but once you want to combine several, it becomes a time sink.

Rube takes care of that. It’s like a central hub where you can run and manage all your MCP servers together. Instead of repeating the same steps for every tool, you set things up once and use them across multiple AI clients like Cursor, Claude, or VS Code.

How developers use it:

Quick setup: connect your apps once and reuse them everywhere
Works across clients: keep the same servers active in Cursor, Claude, or VS Code without reconfiguring
Chain tasks across tools: fetch records from Linear/Jira, email them with Gmail, and post the results on Slack, all in one flow
Share with your team: give teammates access to the same integrations through a single link.
Built-in OAuth: It handles app authentication, so you can focus on getting things done.
Stay secure: Rube is built on Composio, which handles encryption and safe logins.

The value is that you spend less time setting things up and more time using MCP servers for real work. Rube streamlines the complex part of managing servers into a seamless background process.

2. Figma MCP: Design to code without coding

Design files are often where developers lose time. Developers rely on it for layouts, assets, and style guides; however, extracting the correct details from a design file can be a time-consuming process. You should scroll through layers to find a component or ping a designer to confirm spacing. These minor interruptions add up during a sprint.

Figma MCP streamlines the process by allowing you to access design details directly through AI. No waiting on exports or digging through files, the information comes when you ask for it.

What you can do with it:

Access assets: pull icons, images, or components from a file in seconds
Check styles: confirm spacing, colors, and typography without manual digging
Review changes: see what’s different in the latest design update before writing code
Generate snippets: turn design tokens into CSS or components you can use directly
Stay aligned: reduce back-and-forth and keep handoff clear between design and development

The result is faster implementation and fewer blockers. Developers stay focused on building features, and designers spend less time fielding small requests.

3. Context7 MCP: Up-to-date docs for any developer tools

Every developer has faced the frustration of missing context. You open an AI tool expecting a clear answer, but the response feels incomplete because it doesn’t have the right background. Documentation is scattered, code references are buried, and decisions are locked away in chat history. Without the right context, even simple tasks take longer than they should.

Context7 MCP focuses on solving this problem. It gives AI direct access to the information that matters so responses are grounded and accurate. That means when you ask for something, the assistant can pull in the right background from your project and deliver results you can rely on.

Where it adds value:

Provide accurate context: connect AI with the right background before it answers
Reduce noise: filter out irrelevant data and surface only what’s useful
Support RAG workflows: power retrieval-augmented generation for stronger results
Stay current: draw from the latest project data rather than outdated notes
Unify teams: give everyone the same reliable context across tools

The result is less time chasing information and more confidence in the answers you get. Context7 MCP makes AI a dependable partner for development work, not another source of distraction.

4. GitHub MCP: Work with GitHub remotely from IDEs

GitHub is the home base for most developers. It’s where code is stored, where pull requests get reviewed, and where issues are tracked. If you’re building software today, you likely spend a significant portion of your day here.

That also means you know the little pain points. Stopping mid-code to create a bug report. Clicking through the UI to check if a pull request passed tests. Searching for the latest commit or figuring out which PR needs your review. These aren’t hard tasks, but they break your flow and add up over time.

GitHub MCP helps cut through that. By connecting GitHub with AI, you can handle those actions quickly, without leaving your workflow.

Here’s what it can do for you:

Create issues instantly by simply asking the assistant, instead of stopping your work to open GitHub.
Check pull request status and see if tests have passed without digging through the UI.
Fetch the latest commits on any branch while staying in your coding environment.
Summarize open PRs so you know what needs attention right away.
Assign or update issues for your team without context switching.

For developers, this means fewer interruptions and more time focused on actual coding. For teams, it makes staying on top of updates easier and keeps work moving smoothly.

5. Playwright MCP: Automate testing and web

Testing is a part of development that no one can skip, yet it often feels like it slows everything down. Setting up end-to-end tests, debugging failures, and checking coverage can eat up hours that you’d rather spend building features. Even with tools like Playwright, running tests often means leaving your flow to switch into another environment.

Playwright MCP brings testing closer to where developers actually work. Linking Playwright with AI makes writing, running, and analysing tests much more straightforward. Instead of setting everything up manually, you can ask for the tests you need or run existing ones directly through the assistant.

Practical actions it supports:

Run browser tests: execute end-to-end tests on demand without leaving your workflow.
Debug failures: get clear explanations of why a test failed and how to fix it
Generate new tests: create test cases for features or edge cases automatically.
Check coverage: see where your current suite leaves gaps that need attention
Streamline QA: integrate testing into daily work instead of treating it as an afterthought.

The result is more reliable code with less hassle. Developers catch problems earlier, QA teams get stronger coverage, and projects move forward with confidence. Playwright MCP turns testing into a natural part of development rather than a roadblock.

6. Linear MCP: Fetch, edit and complete tasks remotely

Every developer knows the struggle of keeping tasks updated. A bug pops up, but you forget to create a ticket. Priorities shift, and suddenly half the board feels outdated. Someone asks about progress, and you’re digging through issues to piece together an answer. The work of managing the work can start to feel heavier than the coding itself.

This is where Linear MCP steps in. By tying Linear into your AI assistant, the routine updates stop being interruptions. You can log a bug the moment you notice it, check the status of open issues without leaving your editor, or even get a quick snapshot of sprint progress while staying focused on your code.

With Linear MCP, you can:

Create tasks: log bugs or feature requests right as they come up
Update issues: change status, assign teammates, or adjust priorities in seconds
Check progress: get a quick summary of open tasks or sprint status
Spot blockers: see which tasks are holding others back before they become problems
Stay organized: keep the backlog clean and priorities clear without extra effort

The result is less time spent managing boards and more time moving projects forward. Developers can stay focused on building, while teams get a clearer view of what’s happening without relying on constant status meetings.

7. 21st.dev MCP: Make your LLM build beautiful components

Getting code into production is about more than just writing it. Teams need to track progress, manage releases, and make sure nothing slips through the cracks.

21st.dev MCP brings more clarity to that process. It connects development workflows with AI so teams can see progress, release readiness, and velocity without extra reporting overhead. Instead of spending hours compiling updates, you can get a clear picture of project health in just a few seconds.

What it lets you do:

Track releases: monitor what’s ready to ship and what still needs work
Measure velocity: understand how fast your team is moving and spot slowdowns early
Check dependencies: see what tasks or features are blocking progress
Generate insights: create summaries of workload and project health instantly
Stay aligned: keep both developers and managers up to date without manual reporting

The value is simple: developers spend more time coding, and teams get the visibility they need without adding more meetings or dashboards. 21st.dev MCP makes release management feel lighter and keeps projects moving toward production.

8. Supabase MCP: Automate working with Supabase

Every project needs a database, but working with one often feels like a distraction from building features. You might need to write a quick query, add a column, or check permissions, and suddenly you’re bouncing between consoles, docs, and your editor. It’s not hard work, but it does break momentum.

Key things you can do with Supabase MCP:

Run queries: fetch, filter, and test data without juggling consoles
Manage tables: create, modify, or drop tables and columns on the fly
Check authentication: view or adjust user roles and permissions in seconds
Trigger functions: call backend functions whenever you need to validate behavior
Explore schema: see the structure of your database at a glance

By simplifying database work, Supabase MCP makes backend tasks less of a roadblock. Developers can move from idea to implementation faster, without losing time to setup and maintenance.

9. Slack MCP: Send message, get updates, and sumarise chats

Every team relies on Slack, but for developers, it often feels like both a lifeline and a distraction. One moment you’re checking a quick update, and the next you’re pulled into endless threads, side conversations, and notifications. Important context, like a decision about a feature or a bug report, gets buried under gifs and casual chatter. By the time you find what you’re looking for, you’ve already lost focus on your work.

Slack MCP changes how you interact with all that information. It connects Slack to AI, so the flood of messages becomes something you can manage. Instead of wading through channels, you can ask for the key points, the decisions that were made, or the messages that matter to you.

Practical actions it supports:

Summarize threads: turn long discussions into clear takeaways you can read in seconds
Highlight decisions: surface agreements, next steps, and blockers so nothing is missed
Post updates: create reminders or share quick updates directly through the assistant
Track mentions: follow specific channels or keywords to stay in the loop
Stay focused: get the context you need without being pulled into every notification

The benefit is clearer communication and less wasted time. Developers avoid losing hours to endless scrolling, while teams make sure updates and decisions never get overlooked.

10. Jira MCP: Automate solving tickets from IDEs

Jira is a staple for many engineering teams, especially in larger organizations. It’s powerful, but it can also feel heavy. Creating tickets takes time, updating them often feels like busywork, and keeping track of sprint progress can quickly become overwhelming. Developers sometimes spend more time managing Jira than moving actual work forward.

Jira MCP lightens that load. Connecting Jira with AI makes everyday project tracking faster and less disruptive. Instead of stopping to fill in forms or click through dashboards, you can handle tasks with a quick request and keep your focus on building.

Where it adds value:

Create tickets: log bugs, feature requests, or tasks as soon as they come up
Update issues: change status, assign teammates, or adjust timelines on the fly
Check sprint progress: get summaries of what’s open, what’s blocked, and what’s done
Generate reports: pull a clear overview of workload or project health for your team
Stay on track: keep boards current without hours of manual updates

For developers, this eliminates the tedious task of navigating Jira’s interface. For teams, it means tickets stay accurate, sprints stay clear, and project updates become easier to share. Jira MCP transforms Jira from a cumbersome tracker into a tool that supports the workflow quietly.

11. Postman MCP: Interact with APIs

APIs are at the center of almost every modern application. Whether you’re fetching data, sending requests, or debugging endpoints, chances are you spend a lot of time in Postman. The problem is that managing APIs often means constant context switching, running tests in one place, checking logs in another, and keeping track of collections separately.

Postman MCP brings that workflow into one place with AI. You can interact with your APIs through natural language, making it easier to test, monitor, and debug without juggling multiple tools.

How you can use it:

Send requests: call endpoints directly and see responses right away
Run test suites: execute saved collections and confirm results on demand
Inspect responses: get clear explanations of status codes, errors, or payloads
Generate new requests: create endpoint tests for new features quickly
Organize collections: keep your API documentation and test sets structured with less manual work

The outcome is smoother API development. Developers can spot issues earlier, QA teams get stronger testing coverage, and projects move forward without waiting on manual checks.

12. Docker MCP

Containers are everywhere in modern development. They make it possible to run apps consistently across environments, but managing them isn’t always smooth. Starting, stopping, and monitoring containers often means bouncing between terminal commands and dashboards. When deadlines are tight, even simple environmental issues can block progress.

Docker MCP brings container management closer to your daily workflow. It connects Docker with AI, so you can control and monitor containers without leaving what you are doing.

Practical use cases:

Start and stop containers: spin up environments or shut them down with a quick request
Check status: see which containers are running and view their health instantly
Monitor resources: track CPU, memory, and usage patterns in real time
Manage images: pull, tag, or remove images without digging through commands
Debug environments: surface logs quickly to spot problems before they slow you down

Developers can keep environments running smoothly, and teams avoid losing time to setup and troubleshooting. Docker MCP makes containers feel like part of the workflow, not a separate task to manage.

13. LangSmith MCP: Automate agent monitoring

As more teams build with AI, the biggest challenge isn’t just writing prompts, it’s making sure the outputs are reliable. Developers require methods to test, debug, and monitor AI models to ensure they behave as expected in real-world projects. Without the right tools, this means lots of trial and error and very little visibility into why a model succeeds or fails.

LangSmith MCP gives developers a way to bring that process under control. It connects AI assistants directly to evaluation, logging, and testing workflows so you can see what’s happening under the hood and improve your models continuously.

What it unlocks:

Run evaluations: test model outputs against benchmarks or specific criteria
Log interactions: capture real usage data for debugging and iteration
Trace executions: see step-by-step how prompts and responses are handled
Compare outputs: check how different models or prompts perform side by side
Monitor performance: track quality over time and catch regressions early

The value is confidence. Instead of shipping AI features blind, developers can validate, measure, and improve them with the same rigour as any other part of the stack. LangSmith MCP makes AI development more predictable and production-ready.

Key takeaway

MCP has already proven itself as a powerful way to connect developers with the tools they use every day. It eliminates the friction of managing code, tasks, design files, databases, and even testing by allowing AI to handle the busywork. The servers we examined demonstrate the breadth of possibilities, ranging from GitHub and Linear to Playwright and LangSmith. MCP is shaping a workflow that reduces the burden of context switching.

The real takeaway is that MCP isn’t just a convenience. It’s becoming part of the foundation for modern development. The more teams adopt it, the more natural it will feel to let AI manage repetitive actions while developers focus on solving problems and shipping products.

How to set up Trello MCP server with Claude and Cursor for efficient task tracking🚀

Aakash R — Wed, 13 Aug 2025 14:59:32 +0000

Trello is great for keeping projects on track, but it can quickly fill up with cards, long comment threads, and checklists that all need attention. With so much going on, it is easy to lose track of priorities or what should happen next.

Getting AI involved usually means copying text from Trello into a prompt and explaining all the background first. That extra work slows things down and pulls you out of the flow.

Trello MCP makes it simpler. It links Claude and Cursor directly to your Trello boards through Composio, allowing them to work with your actual cards, lists, and comments instantly. The AI understands what is happening and can jump straight into helping without you having to prepare anything.

In this post, you will learn how to set it up and use it to make Trello updates, planning, and follow-ups much easier.

What is MCP?

Think of MCP as a bridge that connects all your SaaS tools to your AI agent. It acts like an adapter, enabling your AI agent (Client) to understand and interact with your tools.

According to Anthropic (the team behind Claude and MCP),

“MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.”

credits: modelcontextprotocol.io

For more, check out this detailed post or MCP components: MCP Explained.

What is Trello MCP?

Trello MCP is a secure connection powered by Composio that lets Claude and similar AI tools work directly with your Trello boards. Once connected, they can see the identical cards, lists, and comments you do and respond based on real information instead of copied snippets.

With Trello MCP, you can:

Read boards, lists, and cards, including members, labels, due dates, checklists, and comments
Create new cards with titles, descriptions, labels, and due dates
Update card details such as descriptions, due dates, and labels, or move them to another list
Search across cards and boards to quickly find what you need
Add comments directly to a card conversation
Look up who is assigned to cards or participating on boards

It turns Trello into a live workspace for your AI, enabling it to help you summarise updates, plan tasks, assign work, and keep projects moving without extra steps.

Setting up Trello MCP with Claude

Connecting Trello to Claude takes only a few minutes. Once it is set up, Claude can pull details from your boards, understand the context of your work, and even post updates back into Trello for you.

What you need before starting

Make sure you have:

Claude Desktop

Download it from claude.ai/download and install it on your computer.
Node.js

This is needed to run the setup command. Check if you already have it by typing node -v in a terminal. If you do not, download it from nodejs.org.

Step 1: Get the setup command from Composio

Visit mcp.composio.dev

Search for Trello and open the integration page

Switch to the Claude tab
Copy the setup command shown, which will look like this:

npx @composio/mcp@latest setup "https://mcp.composio.dev/partner/composio/trello/mcp?customerId=<your_customer_id>" "" --client claude

Step 2: What you can do with Trello MCP

Once Trello MCP is connected, Claude can use a set of ready-to-go actions inside your boards. Here are some of the most useful:

Create new cards in any list on a board
Move cards between lists to match your workflow
Add comments to cards to share updates or ask questions
Assign or remove members from cards
Add or remove labels and due dates
Create and edit checklists inside cards
Search for cards across all boards you can access
Create new lists on a board and move cards into them
Update card titles and descriptions
Archive or delete cards when they are finished or no longer needed

These are all available right away, so you can start automating updates, finding information faster, and keeping boards organised without doing it all manually.

Step 3: Connect Trello from the Claude config folder

Open Claude Desktop
Go to File > Settings, then choose Developer > Edit Config
This will open the Claude config folder on your computer
- On Windows, it is usually here:

C:\Users\<your_username>\AppData\Roaming\Claude

In that folder, open a terminal window
Paste the setup command you copied in Step 1 and run it

npx @composio/mcp@latest setup "https://mcp.composio.dev/partner/composio/trello/mcp?customerId=<your_customer_id>" "" --client claude

A browser window will open asking you to sign into Trello and approve the connection.

After you confirm, you will see a success message in both the browser and your terminal.

Step 4: Confirm the connection (optional)

If you want to double-check that Trello MCP is linked to Claude, open the file claude_desktop_config.json in the same config folder.

You should see Trello listed under "clients". This means the connection is active.

Step 5: Restart Claude

Close the Claude Desktop completely and open it again. This refreshes the settings so Trello MCP is ready to use. Once it starts, you can begin asking Claude to work with your Trello boards right away.

Use case: Summarise card updates and post next steps

Trello cards can collect a lot of activity, updates, decisions, questions, and checklists, which often get mixed together in the comment thread. When you return to a card after a while, it can take some time to read everything and determine what still needs to happen.

With Trello MCP connected, you can ask Claude or Cursor to read the latest activity on a card, extract the key details, and post a clear follow-up right inside that card.

Example prompt

Look at the recent comments on the card “Product Launch Prep”

Summarise the discussion, highlight any open questions and post a follow up in the card

What happens in the background

Claude uses Trello MCP to fetch the comments from that card, sorts through the updates, and writes a short reply with the key points and next steps. It then posts that reply directly as a new comment so the whole team can see it.

▶️ Watch it in action

This video shows Claude reading a real Trello card thread, summarising key updates and posting a proper follow-up in the same conversation. It all happens inside the prompt using live Trello data, without you having to prepare anything manually.

Setting up Trello MCP with Cursor

Linking Trello to Cursor only takes a few minutes. Once it is connected, you can use real board and card data directly inside your prompts without having to copy anything over.

Step 1: Visit the Composio MCP dashboard

Go to mcp.composio.dev and search for Trello. Click the integration to open its details.

Switch to the Cursor tab and click Generate URL. This will give you the exact setup command you need.

Step 2: Copy the command

It will look something like this:

npx @composio/mcp@latest setup "https://mcp.composio.dev/partner/composio/trello/mcp?customerId=<your_customer_id>" "" --client cursor

Step 3: Run the command in the Cursor

Open the Cursor and start a terminal in your current project. Paste the command and press Enter.

A browser window will open asking you to sign into Trello and approve the connection. Follow the prompts to confirm access.

Step 4: Look for the success message

When the setup is complete, you will see a confirmation screen in your browser and a message in your terminal letting you know Trello is connected.

Step 5: (Optional) Double-check the connection

If you want to be sure everything is working, open this file:

~/.cursor/mcp.json

Trello should be listed under connected services. Once you see that, you are ready to start using Trello data directly in Cursor.

Summary

Trello MCP provides Claude and Cursor with direct access to your boards, lists, and cards via Composio. They can read updates, follow conversations, and make changes without you needing to copy anything over.

The setup takes just a few steps. Once connected, you can ask for summaries, follow-ups, planning help, or quick searches, all using live Trello data. It keeps your workflow moving and makes it easier to stay on top of projects.

Frequently asked questions

1. How can I manage my Trello connection after setup

Open the Composio dashboard. You can view, rename, or remove your Trello connection there at any time.

2. What kind of Trello data can Claude or Cursor use

They can work with cards, lists, labels, checklists, due dates, members, and comments you have permission to see in your workspace.

3. Does this work with all boards

Yes. It works with personal boards, shared boards, and team boards. If you can access it in Trello, they can use it.

4. Can I choose which actions are available

Yes. In the Composio dashboard, you can turn actions on or off to match the way you work.

5. Do I need to repeat the setup if I use both Claude and Cursor

Yes. Each tool has its setup step, but they can both connect to the same Trello workspace once linked.

How to setup Airtable MCP for effective work manahement 💻📊

Aakash R — Sat, 09 Aug 2025 04:57:12 +0000

Airtable is a great app for tracking projects, tasks, colloboration, CRM, inventory management, and more. It keeps things clear and organized. But, what if you don’t have to navigate the dashboards to get things done and do things from a single Chat interface.

Airtable MCP makes it possible. It connects your base to tools that understand your tables and fields. You can fetch records, update values, and generate summaries using real data.

In this post, you will see how to set up Airtable MCP with Claude or Cursor and use it to manage updates, track work, and speed things up.

What is MCP?

Think of MCP as a bridge that connects all your SaaS tools to your AI agent. It acts like an adapter, enabling your AI agent (Client) to understand and interact with your tools.

According to Anthropic (the team behind Claude and MCP),

“MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.”

credits: modelcontextprotocol.io

For more, check out this detailed post or MCP components: MCP Explained

What is Airtable MCP

Airtable MCP is a secure connection between your Airtable workspace and tools like Claude and Cursor. It lets large language models work with your Airtable data directly, using a defined set of supported actions.

Once it is connected, these tools can do a lot more than just look at your data, they can actually work with it.

What you can do with Airtable MCP?

Read from your base: They can list records, pull details from tables, check field values, and even understand the base structure.
Create new content: You can ask them to create tables, fields, records, or even leave comments on existing entries.
Update records: They can change the status of tasks, fill in missing fields, or batch update rows across your base.
Delete what you do not need: They can remove records, clear comments, or delete multiple rows at once, if you want them to.
Understand how your base is built: They can fetch the schema and get a sense of your setup, so you do not need to explain every column in your prompt.

With this setup, your Airtable becomes more than a place to store data. It becomes something your tools can actually understand and help you manage, using real structure.

✅ What You will Need

Before you begin, make sure you have these set up:

Airtable Account

If you don’t have one yet, you can sign up at airtable.com. You will use this to create your base and connect it to the integration.
Claude Desktop App

Download and install it from claude.ai/download. This is one of the tools that will connect to your Airtable base.
Cursor Editor

You can download Cursor from cursor.so. It also supports the Airtable MCP integration and works well for coding workflows.
Node.js

You will need this to run the setup script. Install it from nodejs.org, then confirm it’s working by running node -v in your terminal.

🛠 Setting Up Airtable MCP with Claude

Once your tools are ready, you can connect Airtable to Claude using the MCP setup flow from Composio. This lets Claude work with your Airtable data directly.

Step 1: Go to the Composio MCP Page

Head to https://mcp.composio.dev

Search for Airtable, then open the integration page.

Step 2: Copy the Claude Setup Script

On the Airtable integration page, switch to the Claude tab. You’ll see a command that looks something like this:

npx @composio/mcp@latest setup "https://mcp.composio.dev/partner/composio/airtable/mcp?customerId=<your_customer_id>" "" --client claude

Copy the full command, you will use it in the next step.

Step 3: Open Claude Config Folder

Open Claude Desktop
Go to File > Settings > Developer > Edit Config
This will open a folder on your system. On Windows, the path is usually:

C:\Users\<your_username>\AppData\Roaming\Claude

Inside that folder, open a terminal window.

Step 4: Run the Setup Script

Paste and run the setup command you copied earlier:

npx @composio/mcp@latest setup "https://mcp.composio.dev/partner/composio/airtable/mcp?customerId=<your_customer_id>" "" --client claude

This will launch an OAuth2 flow in the terminal. You will be redirected to sign into your slack account and accept permissions.

Once authentication is complete, you can see a screen like this

When it is done, the terminal will show a success message.

Step 5: Confirm the Connection (Optional)

In the same config folder, open claude_desktop_config.json.

You should now see Airtable with the MCP connection details.

Step 6: Restart Claude

Close and reopen Claude Desktop.

You will now see Airtable listed as a connected integration, ready to use inside your prompts.

Use Case: View and Filter Bases Without Leaving Your Workflow

When working across different projects, it can be hard to remember which Airtable base holds what. With Airtable MCP connected, you can ask Claude or Cursor to list all your bases, filter them by name, and get the base IDs you need. Everything stays in one place so you can keep your focus.

Prompt Example

Show me all Airtable bases I have access to and highlight the ones with "content" in the name. Include their base IDs.

🔧 What Happens Behind the Scenes

When you run this prompt, the tool uses the List Bases action from Airtable MCP. It fetches all bases linked to your account, reads their names, and passes them to the model so it can sort or filter them based on your request.

▶️ Watch It in Action

This video shows how Claude or Cursor lists your Airtable bases using live data and helps you select the one you need, all from inside the prompt.

🛠 Setting Up Airtable MCP with Cursor

To connect Airtable with Cursor, follow these steps:

Step 1: Go to the Composio MCP Page

Head over to https://mcp.composio.dev and search for Airtable.

Click on it and switch to the Cursor tab.

Step 2: Copy the Setup Script

You’ll see a command under the Cursor tab. It should look like this:

npx @composio/mcp@latest setup "https://mcp.composio.dev/partner/composio/airtable/mcp?customerId=<your_customer_id>" "" --client cursor

Step 3: Open Cursor in Your Project

Open Cursor and navigate to the folder or file where you want to use the integration.

Step 4: Run the Script in Terminal

Open a terminal inside Cursor, paste the script, and run it.

If you have not connected to Airtable before, an OAuth2 authentication window will appear. Log in to your Airtable account and allow access when prompted. Then it will open a success screen like this:

Once the connection is complete, you will see a success message in the terminal

Step 5: Confirm the Connection (Optional)

To confirm everything worked, you can check the config file at:

~/.cursor/mcp.json

Look for Airtable listed under connected services.

After that, you are all set. Cursor can now work with your Airtable data through MCP, just like Claude.

✅ Summary

Airtable MCP gives Claude and Cursor direct access to your bases, so they can actually work with the data. You can list records, update fields, create tables, or even pull schema details to help the model understand what it is looking at.

With a quick setup through Composio, your Airtable workspace becomes something your tools can use in real time. No more copying rows or explaining columns. Once connected, Claude and Cursor can help you review updates, track work, and make smarter decisions, right from your existing Airtable setup.

FAQs

1. Do I need a paid Airtable plan to use Airtable MCP?

No, a free Airtable account works fine. Just make sure the base you want to connect is in your workspace and accessible.

2. Can Claude or Cursor access all my Airtable data?

Only if you allow it during the authentication step. You choose which bases and workspaces are accessible during setup.

3. What if I have multiple Airtable accounts?

You can connect more than one, but you will need to run the setup script separately for each account and switch between them in your tool if needed.

4. Is this integration secure?

Yes. The connection uses OAuth2 and is permission-based. You control access and can revoke it any time from your Airtable account settings.

5. Can I use Airtable MCP with both Claude and Cursor at the same time?

Yes. You can set up the integration with both tools, and they will each have access to your Airtable data independently once configured.