1
Add it to Claude Code
claude mcp add agentrank -- npx -y agentrank-mcp-server2
Make your agent remember this setup
agentrank's config, env vars, and the gotchas you hit — recalled in every future Claude Code, Cursor, and Codex session.
npx conare@latestFree · one command · indexes the sessions already on disk. Set up in the browser instead →
What it does
- Live, daily-updated index of over 25,000 MCP servers and agent tools
- Scoring engine based on 5-signal composite score (freshness, issue health, dependents, stars, contributors)
- Real-time search capabilities for finding production-ready AI tools
- Detailed repository metadata lookup including issue health and adoption metrics
Tools 2
searchSearches the live index of MCP servers and agent tools based on a query.lookupRetrieves detailed scoring and metadata for a specific repository.Try it
→Find me an MCP server for database access
→Search for the best-ranked tools for web scraping
→Lookup the AgentRank score and health metrics for github.com/modelcontextprotocol/servers
→What are the top-rated MCP servers for file management?
Original README from superlowburn/agentrank
AgentRank
Google PageRank for AI agents. A live, daily-updated index of 25,000+ MCP servers and agent tools — scored by real GitHub signals, not just star counts.
Install in 3 Steps
1. Add to your AI tool:
# Claude Code
claude mcp add agentrank -- npx -y agentrank-mcp-server
2. Ask your AI to find a tool:
"Find me an MCP server for database access"
3. Get ranked, current results — automatically.
That's it. No API key, no config, no prompting. Your AI searches the live index whenever it needs a tool.
Why AgentRank?
- Your AI's knowledge is stale. Training data is months old — it can't know if a tool was abandoned last week or if something better shipped yesterday.
- Stars lie. A repo with 2,000 stars and no commits in 18 months isn't production-ready. AgentRank weighs freshness, issue health, and dependents — not just popularity.
- 25,000+ tools, scored daily. The index crawls GitHub nightly. Every recommendation reflects what's happening now.
- Gets smarter with use. Every query surfaces which tools developers actually reach for, sharpening rankings for everyone.
Demo
> search("postgres mcp server")
1. postgres-mcp Score: 91 ★ 2.1k Updated: 2d ago
2. mcp-server-postgres Score: 84 ★ 891 Updated: 5d ago
3. pg-mcp Score: 71 ★ 432 Updated: 12d ago
> lookup("github.com/modelcontextprotocol/servers")
{
"name": "modelcontextprotocol/servers",
"agentrank_score": 97,
"stars": 14200,
"last_commit": "1 day ago",
"dependents": 1840,
"issue_health": 0.91
}
Links
- agentrank-ai.com — Browse the full index
- API Docs — Use the REST API directly
- Blog — MCP server guides and comparisons
- npm: agentrank-mcp-server — The MCP server package
Repo Structure
crawler/ GitHub crawler — finds and indexes repos nightly
scorer/ Scoring engine — 5-signal composite score (0-100)
mcp-server/ MCP server package published to npm
workers/ Cloudflare Workers API
site/ Astro frontend (agentrank-ai.com)
How the Score Works
Five signals, weighted by signal quality:
| Signal | Weight | What it measures |
|---|---|---|
| Freshness | 25% | Days since last commit — stale repos decay fast |
| Issue health | 25% | Closed / total issues — maintainer responsiveness |
| Dependents | 25% | Repos that depend on this — real-world adoption |
| Stars | 15% | Raw popularity signal |
| Contributors | 10% | Bus factor — solo projects score lower |
Scores are recomputed nightly from live GitHub data.
MIT License · Built by @comforteagle