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Agent Tool Optimization

Are AI agents choosing
your tools?

Research shows 97% of MCP tool descriptions have quality defects. Optimized tools get selected 3.6x more often by AI agents. ToolRank scores your tool definitions and shows you exactly what to fix.

Score your tools — free Learn ATO framework

SEO got you found. LLMO got you cited.
ATO gets you used.

Stage 0

SEO

Human searches Google. Your page appears.

Result: a click
Stage 1

LLMO

Human asks AI. Your brand is mentioned.

Result: a mention
Stage 2+3

ATO

Agent autonomously acts. Your API is called.

Result: a transaction

Four dimensions of agent-readiness

ToolRank Score measures each dimension so you know exactly what to fix.

Findability · 25%

Can agents discover you? Registry presence, tags, llms.txt.

Clarity · 35%

Can agents understand you? Description quality, purpose, context.

Precision · 25%

Is your interface precise? Schema types, enums, error handling.

Efficiency · 15%

Are you token-efficient? Context cost, tool count, modularity.

97.1%of MCP tools have quality defects
3.6xselection advantage for optimized tools
4,000+servers scanned from 2 registries

Sources: arXiv 2602.14878, arXiv 2602.18914. Scan: Smithery + Official MCP Registry.

Scanning the ecosystem daily

ToolRank scans 4,000+ MCP servers from Smithery and Official MCP Registry daily. 73% have no tool definitions — invisible to AI agents. See full ranking →

Live now

  • ✓ Layer 1: Rule-based scoring (14 checks, 4 dimensions)
  • ✓ Layer 2: LLM selection tournament (Claude Sonnet, 100 rounds)
  • ✓ Layer 3: Runtime reliability testing
  • ✓ 3-tier trust verification (Spec / Selection / Runtime)
  • ✓ CI/CD Trust Gates (pre-merge / pre-release / post-deploy)
  • ✓ GitHub Action v2 with deployment gates
  • ✓ Trust Status API + Badge API + Trusted List API
  • ✓ AI-powered rewrite proposals (Pro)
  • ✓ Category rankings
  • ✓ Agent framework SDK (Python)
  • ✓ Daily ecosystem scan (4,000+ servers, 400+ scored)

Coming next

  • ○ Registry partnerships (MCP.so, Smithery trust embedding)
  • ○ Enterprise trust policies & procurement signals
  • ○ PyPI package (pip install toolrank)
  • ○ Continuous runtime monitoring (post-deploy)

Check your score in 10 seconds

Enter your Smithery server name, GitHub repo URL, or paste JSON. Get your ToolRank Score with specific fixes.

Score your tools — free

Resources

ATO Framework

3 stages, 4 dimensions, before/after examples

Ecosystem Ranking

Live scores for all scored MCP servers

Score Badge

Add ToolRank to your README

Blog

ATO insights and ecosystem analysis

Frequently asked questions

What is ATO (Agent Tool Optimization)?

ATO is the practice of optimizing your tools, APIs, and services so AI agents can discover, select, and execute them autonomously. LLMO covers only Stage 1. ATO is the complete picture.

How is ATO different from LLMO?

LLMO optimizes for mentions. ATO optimizes for execution — getting your API actually called by agents. The difference is between advertising and transactions.

What is ToolRank Score?

A 0-100 metric measuring how likely AI agents are to discover and select your MCP tools. Four dimensions: Findability, Clarity, Precision, Efficiency. Optimized tools achieve 72% selection probability versus 20% baseline.

Is ToolRank free?

Yes. Score diagnosis is free. The ATO framework and scoring logic are open source.