Growth

Is AI ignoring your website? growth-mcp is an open-source MCP server that turns Claude, Cursor, or any MCP client into a marketing analyst — starting with the question every marketer is asking in 2026: can ChatGPT, Perplexity, and Google AI Overviews actually read and cite my site?

CI License: MIT Python 3.11+

No API keys. No signup. No LLM calls from the server. Just ask your AI assistant:

"Audit example.com for AI search readiness"

and get back a 0–100 score with evidence and a prioritized fix list:

AI-readiness score: 62/100 (C)
✅ Heading hierarchy is clean
✅ robots.txt does not block AI crawlers
❌ No JSON-LD structured data — AI engines can't parse your entities
❌ No llms.txt — AI crawlers have no guide to your site
❌ Content only appears after JavaScript renders — invisible to most AI crawlers

Why this exists

Search is shifting from "rank on Google" to "get cited by AI." Paid GEO tools cost $100–1000/month; Google's free tools validate markup but don't score or guide. growth-mcp gives you the audit for free, inside the AI assistant you already use — with deterministic, explainable scoring (same URL → same score).

Quick start

Claude Code

claude mcp add growth-mcp -- uvx growth-mcp

Claude Desktop / Cursor

Add to your MCP config (claude_desktop_config.json or .cursor/mcp.json):

{
  "mcpServers": {
    "growth-mcp": {
      "command": "uvx",
      "args": ["growth-mcp"]
    }
  }
}

Requires uv. From source instead: clone this repo and use "command": "uv", "args": ["run", "--directory", "/path/to/growth-mcp", "growth-mcp"].

Tools

Tool	What it does
audit_ai_readiness(url)	15-check GEO audit → score/100, grade, evidence, fix list
compare_ai_readiness(url_a, url_b)	You vs. competitor — scores, winner, per-check diff
check_llms_txt(domain)	Finds and validates llms.txt against the llmstxt.org format
audit_onpage_seo(url)	Title/meta, H1s, canonical, Open Graph, alt coverage, links

The 15 AI-readiness checks, across four dimensions:

• Access & indexability (30%) — AI-crawler access in robots.txt (GPTBot/ClaudeBot/PerplexityBot blocked?) · JS-rendered-content risk · noindex/nosnippet directives · XML sitemap
• Entity & structure (25%) — JSON-LD structured data · heading hierarchy · title/meta quality · llms.txt presence
• Citation-ready content (30%) — FAQ content & FAQPage schema · question-based headings · answer-first structure · lists & tables · factual density (stats/numbers)
• Trust & E-E-A-T (15%) — author attribution · freshness dates

Prompts included: full_site_audit, compare_with_competitor — available as slash-commands in clients that support MCP prompts.

Example prompts

• "Is my site ready to show up in AI search results? Check leapswitch.com"
• "Compare my homepage with competitor.com for AI visibility"
• "Does my robots.txt block AI crawlers?"
• "Validate the llms.txt on my domain"

Run as a hosted endpoint

The same server runs as a remote MCP service over streamable HTTP:

growth-mcp --transport streamable-http --port 8000
# or
docker build -t growth-mcp . && docker run -p 8000:8000 growth-mcp

Deploy it on any VPS or cloud server — for example on Leapswitch — and point any MCP client at http://your-host:8000/mcp.

Design principles

• Stateless data-plumbing — the server never calls an LLM; your AI client is the brain.
• Deterministic scoring — pure Python, weighted, versioned (scoring_version in every result). No flaky AI grades.
• Safe fetching — SSRF guard (private/loopback/link-local IPs rejected on every redirect hop), 5 MB response cap, honest User-Agent, timeouts.
• Compact output — tools return structured JSON (score + evidence + fix), never raw HTML, so your context window stays clean.
• Contributor-friendly — every audit rule is one small file in growth_mcp/checks/. Add a function, decorate with @register, done.

Roadmap

• v0.2 — Connectors (bring your own keys): Google Search Console top queries, GA4 reports.
• v0.3 — AI visibility tracking: run your target prompts against ChatGPT / Perplexity / Gemini APIs and report whether your brand is mentioned and cited vs. competitors — the $100/mo SaaS feature, open source.
• Playwright-based deep rendering (optional extra), sitemap-wide audits, more checks.

Contributing

New checks are the easiest contribution — see the pattern in growth_mcp/checks/base.py. Run the test suite with:

uv sync --dev
uv run pytest

License & credits

MIT © Abhishek Ambad. Built with support from Leapswitch Networks.