Team Builds 60+ AI MCP Servers, Finds Evaluation Loops Beat Feature Additions
The team behind tancoai.com spent several months building over 60 Model Context Protocol (MCP) servers and AI Skills, initially following a simple build-test-ship cycle. After analyzing more than 100 failed Skill implementations, they found that 67% suffered from trigger mismatches and 54% had retrieval problems, yet none had any feedback mechanism in place. This prompted them to develop what they call the Five Elements Framework, a structured approach to Skill design focused on reliability rather than feature volume. The framework emphasizes measurable criteria such as trigger precision, context retrieval hit-rates, and output consistency before any Skill is allowed to ship. Their key takeaway was that improving evaluation loops proved more impactful than continuously adding new capabilities to AI agents.



