Agent Tool Optimization
Are AI agents choosing
your tools?
97% of MCP tool descriptions have quality defects.
Optimized tools get selected 3.6x more.
ToolRank scores and fixes your tool definitions so agents pick you first.
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 separately 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, usage 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 description defects
3.6x
selection advantage with optimization
10,000+
MCP servers competing for selection
Sources: arXiv 2602.14878, arXiv 2602.18914, Anthropic MCP ecosystem data
Check your score in 10 seconds
Paste your MCP tool definition or server URL. Get your ToolRank Score with specific fixes.
Score your tools — freeFrequently 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. Unlike SEO (for search engines) or LLMO (for LLM citations), ATO focuses on the complete agent selection pipeline: being recognized (Stage 1), being chosen over competitors (Stage 2), and being used reliably (Stage 3). LLMO covers only Stage 1. ATO is the complete picture.
How is ATO different from LLMO?
LLMO optimizes for mentions — getting your brand cited in AI responses. ATO optimizes for execution — getting your API actually called by agents. The difference is between advertising and transactions. LLMO is necessary (agents need to know you exist) but not sufficient (knowing about you doesn't mean choosing you). ATO encompasses LLMO as its first stage and adds selection optimization and execution quality.
What is ToolRank Score?
ToolRank Score is a 0-100 metric measuring how likely AI agents are to discover and select your MCP tools. It evaluates four dimensions: Findability, Clarity, Precision, and Efficiency. Academic research shows that MCP tools with optimized descriptions achieve 72% selection probability versus a 20% baseline — a 3.6x advantage. ToolRank Score quantifies this advantage.
Is ToolRank free?
Yes. The ToolRank Score diagnosis is free (5 per month). The ATO framework and scoring logic are open source. Pro features (unlimited diagnosis, automatic rewriting, monitoring) are available via paid plans.