The first production failure is usually not dramatic. A tool call times out, the model gives the user a vague apology, and nobody can tell whether the MCP server crashed, the upstream API slowed down, the client sent bad arguments, or a proxy dropped the stream.

The gap between a demo MCP server and a production MCP server is not the number of tools. It is the infrastructure around every tool call: authorization, input validation, rate limits, timeouts, structured logs, metrics, graceful shutdown, and clear error semantics.

This lesson covers those patterns with Python code you can adapt. Treat the examples as building blocks, not a complete framework.