ToolRank
About
Score and optimize MCP tool definitions for AI agent discovery. Analyzes Findability, Clarity, Precision, and Efficiency.
Details
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- SSE
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Setup
Install ToolRank in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/imhiroki/toolrank
Follow the installation instructions in the repository README, then restart your MCP client.
toolrank_score
Analyzes MCP tool definitions and returns a ToolRank Score (0-100) measuring agent-readiness. Evaluates four dimensions: Findability (25%), Clarity (35%), Precision (25%), and Efficiency (15%). Use this when you want to check or improve the quality of your MCP tool definitions. Returns per-tool scores, maturity level, specific issues found, and fix suggestions ranked by impact.
toolrank_suggest
Generates specific improvement suggestions for MCP tool definitions to increase their ToolRank Score. Use this when you have a low score and want actionable fix recommendations. Returns rewritten description, improved schema, and estimated score improvement for each suggestion.
Score, optimize, and monitor how AI agents discover and select your MCP tools.
Score Your Tools →·Framework·Ranking·Blog
We scanned 4,162 MCP servers. Here's what we found.
73% of MCP servers are invisible to AI agents.They have no tool definitions, no descriptions, no schema. When an agent searches for tools, these servers don't exist.
Sources:arXiv 2602.14878,arXiv 2602.18914
ATO (Agent Tool Optimization)is to the agent economy what SEO was to the search economy.
LLMO is Stage 1 of ATO — necessary but not sufficient.
toolrank.dev/score— paste your tool JSON or enter your Smithery server name.
from toolrank_score import score_server, format_report result = score_server("my-server", tools) print(format_report(result))
- "name": "get", - "description": "gets data from the api" + "name": "search_repositories", + "description": "Searches for GitHub repositories matching a query. + Useful for finding open-source projects or checking if a repo exists. + Returns name, description, stars, language, and URL.", + "inputSchema": { + "type": "object", + "properties": { + "query": { "type": "string", "description": "Search query" }, + "sort": { "type": "string", "enum": ["stars", "forks", "updated"] } + }, + "required": ["query"] + }
Score: 52 → 96.Five minutes of work. 3.6x selection advantage.
toolrank/ ├── packages/ │ ├── scoring/ # Level A engine (Python, zero-cost) │ │ ├── toolrank_score.py # 14 checks across 4 dimensions │ │ ├── level_c_score.py # Claude AI scoring (Pro) │ │ └── weights.json # Auto-calibrated weights │ ├── scanner/ # Ecosystem scanner │ │ ├── scanner_v3.py # Weekly full / daily diff │ │ ├── calibrate.py # Weight auto-adjustment │ │ └── auto_blog.py # Daily article generation │ ├── web/ # Astro site (toolrank.dev) │ ├── mcp-server/ # ToolRank MCP Server │ └── badge-worker/ # Dynamic badge SVG (CF Workers) └── .github/workflows/ # Automated pipelines
ToolRank is open source. The scoring logic is fully transparent and auditable.
- Report issues:GitHub Issues
- Scoring methodology:packages/scoring/toolrank_score.py
- Governance:GOVERNANCE.md·CHANGELOG.md
- ATO Framework:toolrank.dev/framework
⭐Star this repoif you find ToolRank useful — it helps others discover it.
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