- AEO for tools means making them easy for an agent to choose.
- The agent reads names, descriptions, and schemas to decide.
- Discoverability is measurable via adoption and success rate.
Answer engine optimization (AEO) usually means making content easy for AI to cite. For an MCP server, there is a parallel discipline: making your tools easy for an AI to discover, choose, and call correctly. The 'answer engine' here is the agent, and your tools are what it reaches for.
The agent is your audience
A model reads your tool names, descriptions, and schemas at the moment it decides what to do. If that text is vague, the tool is effectively invisible, no matter how good the implementation is.
Optimize for the moment of choice
- Clear names: verb + object, matching how a user phrases intent
- Descriptions that state when to use and when not to
- Schemas that accept the shapes models naturally produce
- No overlapping tools competing for the same intent
Measure discoverability
AEO for tools is measurable. Track adoption per tool and success rate after a description change. If the right tool starts getting called for the right intent, your optimization worked.
For an MCP server, the best-optimized tool is the one the agent picks correctly without a retry.
See this on your own server
TrackMCP turns your MCP server's calls into adoption, workflows, and outcomes. One line to install.