Why I Added a Dynamic JSON Validator to My MCP Hub

Cybergail — Wed, 20 May 2026 05:53:40 +0000

While building my MCP server hub, I noticed a common problem:

Every MCP server exposes different tools, and every tool expects different input formats.

A weather tool may need:

json id="5hghsy"
{
"city": "Chennai"
}

While a GitHub tool may need:

json id="u7clz5"
{
"repo": "my-project",
"title": "Bug report"
}

At first this sounds simple.

But once you start connecting dozens or hundreds of MCP servers, things get messy very quickly.

How does the system know:

which fields are required?
which values are valid?
whether the AI formatted the request correctly?
or whether a tool call is missing important data?

That’s why I added a Dynamic JSON Validator as one of the tools inside my MCP hub.

What the Tool Does

In simple terms, the validator acts like a smart checker.

Before a request reaches a tool, it verifies:

the structure of the JSON,
required fields,
data types,
missing values,
and invalid formats.

If something is wrong, the system catches it early instead of letting the tool fail unexpectedly.

Why “Dynamic” Matters

The important part is that the validator is dynamic.

The rules are not hardcoded.

Instead, the validator can adapt automatically depending on:

the MCP server,
the tool being used,
or the schema provided at runtime.

That means new MCP servers can be added to the hub without manually rewriting validation logic every time.

Why This Is Useful

Without validation:

tools crash,
AI systems send malformed requests,
debugging becomes difficult,
and integrations become fragile.

With validation:

tools become safer,
integrations become scalable,
onboarding becomes easier,
and AI interactions become more reliable.

The Bigger Picture

Most people think MCP ecosystems are only about AI tools.

But infrastructure layers like validation are what actually make those ecosystems usable at scale.

As MCP ecosystems continue growing, I think dynamic validation will become a core part of reliable AI tooling.

AI Agents Are Wandering a Growing World of MCP Tools With No Map — So I’m Building One

Cybergail — Mon, 18 May 2026 07:26:08 +0000

Have you ever wondered where all the tools for AI agents actually are?

Right now, new MCP servers are being built every day—tools that let AI agents interact with files, databases, Slack, websites, APIs, and real-world systems—but most of them are scattered across GitHub repos, Discord chats, and random posts online. Unless you already know exactly what you’re looking for, discovering useful MCP tools is surprisingly difficult.

That’s the problem I wanted to solve by building a simple MCP server directory where creators can submit a name, description, and link so their tools can actually be discovered in one place.

The platform is intentionally simple. There’s a clean homepage explaining the idea, a lightweight login page for contributors, and a central page where all MCP servers are listed and searchable. Instead of digging through random repositories and posts, developers can quickly browse tools built for AI agents in one organized place.

What makes this interesting is that it’s not just organizing software—it’s organizing what AI systems can do. These MCP tools let AI agents connect to things like files, databases, Slack, websites, and APIs. In a way, the directory becomes a growing map of AI capabilities and integrations.

The main users are AI developers looking for ready-made tools, MCP creators who want visibility for their work, and curious builders exploring what AI agents can actually do beyond chatting. It’s a small idea, but it solves a real problem: turning a scattered ecosystem into something searchable, understandable, and easy to explore.

Forem: Cybergail

Why I Added a Dynamic JSON Validator to My MCP Hub

What the Tool Does

AI Agents Are Wandering a Growing World of MCP Tools With No Map — So I’m Building One