MIME Type Grounding Data for AI Agents

Overview

A MIME type and file extension lookup API, for example the MIME type for JSON or the extensions behind image/png. MIME Type Lookup works by searching the mime-db database, which merges the IANA, Apache and nginx type lists. Returns category and default charset for an extension or MIME type, with the defining source and all associated extensions on paid plans, and accepts a full Content-Type header value such as text/html; charset=utf-8.

Live Test MIME Type Grounding Data for AI Agents Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as MIMETypeGroundingDataforAIAgents. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

{
  "name": "MIMETypeGroundingDataforAIAgents",
  "arguments": {
    "extension": "pdf"
  }
}

You do not name the tool yourself; the model picks it. Asking about pdf in the terms this source covers is enough for it to reach for MIMETypeGroundingDataforAIAgents on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every source in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervecontext": {
      "url": "https://api.vervecontext.com/v1/mcp"
    }
  }
}

https://api.vervecontext.com/v1/mcp

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

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{
  "status": "ok",
  "error": null,
  "data": {
    "count": 1,
    "results": [
      {
        "extension": "jpg",
        "mimeType": "image/jpeg",
        "category": "image",
        "charset": null,
        "compressible": false,
        "source": "iana",
        "extensions": [
          "jpg",
          "jpeg",
          "jpe"
        ]
      }
    ]
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Why ground on it

A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.

Point an evaluation at count: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

A call costs 1 credit each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

File Upload Handling

Validate and identify file types during file upload processing in web applications

Content-Type Headers

Set correct Content-Type headers for HTTP responses based on file extensions

File Processing

Determine how to process files based on their MIME type in data pipelines

API Development

Configure proper content type handling in REST APIs and file serving endpoints

Other ways to use MIME Type Grounding Data for AI Agents

Set up MIME Type Grounding Data for AI Agents on VerveContext, or reach the same source a different way. Your VerveContext account and credits work on all of them — one key, one balance.

Related

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