Bot Detection Grounding Data

The tool

Ground agents in a verifiable database of known bots and crawlers so traffic and access decisions rest on real reputation data, not guesses.

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

{
  "name": "BotDetectionGroundingData",
  "arguments": {
    "ua": "Googlebot/2.1 (+http://www.google.com/bot.html)"
  }
}

What that looks like in a conversation

You don't name the tool — the model picks it. Asking about Googlebot/2.1 (+http://www.google.com/bot.html) in the terms this source covers is enough for it to reach for BotDetectionGroundingData on its own.

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 Desktop, 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.

What the model gets back

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

{
  "status": "ok",
  "error": null,
  "data": {
    "userAgent": "Googlebot/2.1 (+http://www.google.com/bot.html)",
    "isBot": true,
    "bot": {
      "name": "Googlebot",
      "category": "search_engine",
      "url": "http://www.google.com/bot.html",
      "reputation": "trusted",
      "shouldBlock": false
    },
    "isAutomated": true,
    "riskScore": 0,
    "riskLevel": "low"
  }
}

Fields

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A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong.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 with 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 userAgent: 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 with a sentence the model can act on, not a bare status code.

Other ways to use Bot Detection Grounding Data

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

Frequently asked questions