Build an MCP Server That Finds Your RAG Chatbot's Blind Spots

RAG chatbots fail silently. Imagine you've hired a chatbot to handle FAQs on your behalf, so you can level up your D&D character instead of replying to the same questions over and over. You provided all the information you could think of. Then your potential biggest client asks if you're left or right handed. The chatbot doesn't know. The client walks away. It never occurred to you to teach it. Who asks that? You will never know. Because you had no idea there was a missed question in the first place. In this tutorial, we're building an MCP server that creates that feedback loop. When the chatbot gives a low-confidence answer, it logs the question. Over time, questions pile up. Run the pattern analysis and it clusters them by semantic similarity — "oh, 40 people asked about refund policies in different ways, and we have zero documentation on that." Then it suggests what to write, and you close the loop. We'll build it step by step. By the end, you'll have a working MCP server with four