Geo Inspector MCP Server

geo-inspector-mcp

An MCP server that gives an AI assistant four tools for inspecting how a website presents itself to other AI systems: which AI crawlers it blocks, whether it publishes llms.txt, what schema markup it ships, and how its indexing directives are set.

Published on npm and listed in the official MCP Registry as io.github.Bigsupe55/geo-inspector-mcp. Works with Claude Code, Claude Desktop, or any MCP client.

Tool output above is verbatim from a live run against nytimes.com, docs.anthropic.com, and stripe.com, captured by driving the built server over MCP stdio. The sitemap list is collapsed to a count; nothing else is edited. Regenerate with npm run demo.

Why this exists

AI assistants are becoming a primary way people find and cite content, and sites signal their intent to AI systems through a handful of plumbing files: robots.txt rules for AI crawlers, the emerging llms.txt standard, schema.org structured data, and meta directives. Checking those by hand means juggling curl, a robots.txt parser in your head, and view-source. This server turns all of it into questions you can just ask Claude.

Quickstart

npx -y geo-inspector-mcp

That is the whole install. Point your MCP client at it:

Claude Code

claude mcp add geo-inspector -- npx -y geo-inspector-mcp

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "geo-inspector": {
      "command": "npx",
      "args": ["-y", "geo-inspector-mcp"]
    }
  }
}

Then ask things like: "Which AI crawlers does nytimes.com block?" or "Does stripe.com publish an llms.txt?"

Tools

Every tool returns a readable summary plus structured JSON (structuredContent) for programmatic use.

How it is built

The interesting part of an MCP server is not the tools, it is the contract around them.

Every tool returns two things. A readable summary for the model to reason over, and structuredContent for anything downstream that needs to compute. That split is what lets a separate scoring layer (ai-visibility-audit) derive deterministic numbers from the same call the model is reading in prose. The model never has to parse its own tool output back into data.

Parsers are pure functions. robots.txt, llms.txt, JSON-LD, and meta directives each parse in isolation, with no I/O and fixture-based tests. A tool handler is a thin shell: fetch, parse, format. This is what makes the behavior testable without a network, and it is why the test suite runs with no fixtures to record and no site to hit.

All network access goes through one helper. A single fetch path with a size cap, a redirect limit, and a timeout. An MCP server runs inside someone else's agent loop with their API budget attached, so a tool that can hang or stream an unbounded response is a tool that can ruin a session. One choke point means those limits cannot be forgotten in a new tool.

robots.txt parsing follows RFC 9309 rather than a regex, because the whole value of the tool is being right about whether a specific crawler is allowed. Longest-match wins, user-agent groups merge, and Allow can override a broader Disallow.

Development

npm install
npm test        # vitest unit + integration tests
npm run build   # bundle to dist/
npx @modelcontextprotocol/inspector node dist/index.js   # poke it interactively

Parsers are pure functions with fixture-based tests; all HTTP goes through one capped, redirect-limited fetch helper.

Regenerating the demo GIF

npm run demo          # build, capture, render

Or the steps separately:

npm run demo:capture  # drives the built server over MCP stdio, writes scripts/demo-data.json
npm run demo:render   # renders docs/demo.gif from that JSON (needs: pip install Pillow)

demo-data.json is committed, so rendering works offline and the GIF's claims stay auditable: diff it against what the tools return today. The capture script mocks nothing, so if a site changes its robots.txt, the demo changes with it. The only authored text in the pipeline is the human question line; the sites to inspect are configured at the top of scripts/capture-demo.mjs.

Related

ai-visibility-audit is the workflow layer on top of this one: it orchestrates these four tools into a scored, client-ready report. This repo is the raw tools.

License

MIT, see LICENSE.