I Built 2 Production MCP Servers — Here's What I Learned

I Built 2 Production MCP Servers — Here's What I Learned Most MCP tutorials stop at "hello world." Here's what happens when you build the real thing. I've built two production MCP (Model Context Protocol) servers over the past few months: OathScore — 8 tools for AI trading agents. Real-time exchange status, volatility data, economic events, API quality ratings. Live at api.oathscore.dev . Curistat — 10 tools for volatility forecasting. Regime detection, directional signals, session planning. Launching March 2026. Both are built with Python, FastMCP, and FastAPI. Both are deployed on Railway. Both are listed in MCP directories. Here's everything I learned that the tutorials don't tell you. 1. Tool Design Matters More Than Code The hardest part of building an MCP server isn't the code — it's deciding what the tools should be. AI agents read your tool descriptions to decide when to call them. If the description is vague, the agent won't use it. If you make too many tools, the agent gets c