growth-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@growth-mcpAudit example.com for AI search readiness"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
growth-mcp
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?
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 crawlersWhy 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).
Related MCP server: checkyourself
Quick start
Claude Code
claude mcp add growth-mcp -- uvx growth-mcpClaude 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 |
| 15-check GEO audit → score/100, grade, evidence, fix list |
| You vs. competitor — scores, winner, per-check diff |
| Finds and validates |
| 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-mcpDeploy 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_versionin 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 pytestLicense & credits
MIT © Abhishek Ambad. Built with support from Leapswitch Networks.
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