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Inspect a site's AI-search readiness: AI crawler access, llms.txt, schema markup, meta directives
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Inspect a site's AI-search readiness: AI crawler access, llms.txt, schema markup, meta directives
Security Report
Valid MCP server (1 strong, 0 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
12 files analyzed · 1 issue found
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How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-bigsupe55-geo-inspector-mcp": {
"args": [
"-y",
"geo-inspector-mcp"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
geo-inspector-mcp
Inspect any website's AI-search readiness from Claude (or any MCP client): which AI crawlers it blocks, whether it publishes llms.txt, what schema markup it ships, and how its indexing directives are set.
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
| Tool | What it checks | Example question |
|---|---|---|
check_robots_txt | Which AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot, and more) are allowed or blocked, per RFC 9309, plus sitemaps | "Can OpenAI train on example.com?" |
fetch_llms_txt | Presence and spec-validity of /llms.txt and /llms-full.txt | "Has example.com adopted llms.txt?" |
detect_schema_markup | JSON-LD blocks, schema.org type inventory, AI-relevant types, sameAs disambiguation | "What structured data does this article have?" |
check_meta_directives | Meta robots tags (including noai/noimageai and bot-specific tags) and X-Robots-Tag headers | "Is this page indexable?" |
Every tool returns a readable summary plus structured JSON (structuredContent) for programmatic use.
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.
License
MIT
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