As AI technologies continue to evolve, developers are constantly seeking ways to enhance and streamline interactions with large language models (LLMs). One significant advancement in this space is the Model Context Protocol (MCP), recently introduced by Anthropic, and its implementation through MCP servers. In this article, I'll share my findings and experiences with MCP servers, exploring what they are, why they matter, and how you can get started with them.
What is the Model Context Protocol?
The Model Context Protocol (MCP) is a standardized communication protocol that defines how applications interact with language models while efficiently managing context. It was designed to address the limitations of traditional API interactions with LLMs, particularly around context management.
The protocol enables developers to create applications that can have more natural, ongoing conversations with AI models without constantly hitting context limitations.
What is an MCP Server?
An MCP server is a specialized server that implements the Model Context Protocol, designed to enhance interactions with LLMs by managing contextual information more efficiently. In essence, it serves as a middleware between your application and language models, providing optimized context handling, memory management, and state persistence across interactions.
Unlike traditional API calls to language models where context is handled within each request, an MCP server maintains context as a first-class citizen, allowing for more sophisticated and efficient AI interactions.
Why Would I Need an MCP Server?
The need for MCP servers arises from several limitations in standard LLM interactions. Here is a comparison between traditional LLM calls and MCP Server Approach.
Traditional LLM Calls 🆚 MCP Server Approach
| Feature | Traditional LLM API Calls | MCP Server Approach |
|---|---|---|
| Context Management | Manually append full history; prone to overflows | Context is intelligently summarized and pruned by the server |
| Memory Efficiency | Sends entire conversation each time | Only sends what's relevant, with long-term memory persistence |
| Cost Optimization | High token usage due to repeated context | Reduced tokens via smart context windows and state reuse |
| Enhanced Functionality | Limited to basic Q&A or single-turn logic | Supports retrieval-augmented generation, tool use, and multi-turn sessions |
| Consistent Performance | Degrades with long interactions or context resets | Maintains consistent response quality via controlled context |
If you're building applications that require ongoing conversations with AI models, persistent memory, or sophisticated context management, an MCP server could be invaluable to your architecture.
What Makes a Server an MCP Server?
A server becomes an MCP server when it implements the Model Context Protocol specifications. Key characteristics include:
- Context Management: Implements strategies for context maintenance, summarization, and relevance determination.
- Protocol Implementation: Adheres to the MCP API specifications for communication with clients.
- State Persistence: Maintains conversation state across multiple interactions.
- Memory Management: Implements strategies for what to remember, summarize, or forget.
- Message Routing: Efficiently routes messages between clients and language models.
- Optional Features: May include retrieval-augmented generation, tool use, or other enhanced capabilities.
The core differentiator is the server's implementation of the protocol's standards for handling context as a managed resource rather than merely passing messages.
What Company or Organization is Behind It?
The Model Context Protocol was created by Anthropic, the AI research company behind the Claude language model. They introduced MCP to provide developers with a standardized way to build applications that could more effectively maintain conversations with their AI models.
Anthropic has made the MCP specification open for implementation, encouraging the development of various server implementations and client libraries across the ecosystem.
What are the Known Limitations of MCP Servers?
Despite their advantages, MCP servers do have several limitations:
Implementation Complexity: Setting up and maintaining an MCP server requires more infrastructure than simple API calls.
Latency Concerns: The additional processing layer can introduce latency in some implementations.
Resource Requirements: Running an MCP server requires additional computational resources compared to direct API calls.
Standardization Challenges: As the protocol evolves, different implementations may support different feature sets.
Model Compatibility: Not all language models are fully optimized for MCP-style interactions.
Security Considerations: Persistent state management introduces additional security concerns regarding data storage and access.
Cost Structure: While potentially reducing token usage, running the server itself adds operational costs.
These limitations don't diminish the value of MCP servers but are important considerations when deciding whether to implement one in your AI architecture.
Are There Similar Alternatives That Solve the Same Kind of Problem?
Several alternatives address similar context management challenges:
LlamaIndex: Offers advanced retrieval and indexing capabilities for managing context with LLMs.
Custom RAG Solutions: Many teams build custom retrieval-augmented generation systems that manage context through vector databases.
Context Compression Techniques: Some solutions focus on compressing context rather than managing it through a protocol.
Function Calling Frameworks: Frameworks that orchestrate complex interactions between models and tools.
🛠️ Function Calling Frameworks for LLMs
| Framework / Tool | Description | Ecosystem | Good For |
|---|---|---|---|
| OpenAI Function Calling | Native support for structured function execution via JSON schema | OpenAI API | Simple apps, single-agent LLM + tools setup |
| LangChain | Python/JS framework to build chains, tools, and autonomous agents with function calling | Open-source | Agent workflows, tool orchestration, RAG |
| Semantic Kernel | Microsoft’s SDK for building AI copilots using plugins, skills, and memory | Microsoft / Azure | Enterprise apps, plugin-based agent design |
| AutoGen | Multi-agent system framework from Microsoft for LLM collaboration | Open-source (MS Research) | Agent collaboration, task decomposition |
| CrewAI | Orchestrates LLM agents as a “crew,” each with a role and tools | Open-source | Role-based multi-agent workflows |
| Google Vertex AI Extensions | Allows function/tool integration in Vertex AI models (tools like web search, APIs) | Google Cloud Platform | GCP-native AI tool chaining |
| Hugging Face Transformers Agents | Tool-using agents powered by Hugging Face pipelines | Hugging Face | Open-source experimentation, local models |
The main difference is that MCP represents a standardized protocol specifically designed for context management, while many alternatives are frameworks or techniques that can be used to implement similar functionality but lack the standardization benefits.
How Can I See an MCP Server in Action?
To experience an MCP server firsthand, you can take a look at these amazing resources:
To get a quick feel for it, I recommend starting with a simple local implementation that connects to a model you already have access to, such as Claude via Anthropic's API.
Can I Build My Own MCP Server and What Would I Need?
Yes, you can build your own MCP server with the following requirements:
Technical Requirements:
Programming Knowledge: Familiarity with a language suitable for server implementation (Python is common).
API Access: Access to an LLM API (Anthropic's Claude, OpenAI's GPT, etc.). Or if you prefer to run the LLM locally you should definitely checkout ollama
Server Environment: A system to host your server (can be local for development).
Database: For persistent storage of conversation history and context (PostgreSQL, MongoDB, etc.).
Understanding of Protocol: Familiarity with the MCP specification.
Implementation Steps:
- Start with one of the reference implementations or starter repositories.
- Set up your development environment and dependencies.
- Configure your model API credentials.
- Implement the core protocol endpoints.
- Add context management strategies based on your use case.
- Test with simple client applications.
- Optimize for your specific requirements.
Building a basic MCP server can be accomplished in a weekend for experienced developers, but creating a production-ready implementation with advanced features will require more significant investment.
Links
https://www.anthropic.com/news/model-context-protocol
https://modelcontextprotocol.io/introduction
https://mcp.so/
https://www.serverless.com/framework/docs/guides/mcp
https://github.com/modelcontextprotocol/servers
Top comments (2)
Great Write up!
I also made a video around MCP:
Great explanation!