About this talk
A walkthrough of the MCP discoverability framework: three foundations (be findable, deliver value, be loved) and two measurement layers, static discovery and dynamic discovery, across AI clients.
Key points from “The MCP App Discoverability Framework”
- Static discovery asks whether users find you for the keywords that matter.
- Dynamic discovery asks whether you win the prompts that matter.
- Registry presence, tool metadata, performance and engagement all feed the same optimize-and-measure cycle.
Prefer to read? The MCP discoverability framework covers the same ground in writing.
Related reading
- The MCP discoverability framework. Three foundations and two measurement layers: static and dynamic discovery.
- ChatGPT Plugin discovery: how to get suggested. How ChatGPT decides which Plugins to suggest, and how to get yours picked.
- How to get your Claude Connector discovered organically. A four-stage process to measure, diagnose, improve and verify Claude discovery.
- MCP Directory: every ChatGPT Plugin and Claude Connector. Search and compare listings, ranked and updated daily.