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.
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