I've reviewed dozens of custom MCP servers built by developers connecting AI assistants to their internal tools. The build tutorials are everywhere — the mistake patterns aren't.

Here are the five most common mistakes that make MCP servers unreliable, slow, or silently broken.

TL;DR

# Mistake Impact Fix

1 Printing to stdout Server disconnects immediately Route all diagnostics to stderr

2 Vague tool descriptions AI calls wrong tools or hallucinates params Write descriptions the AI reads at call time

3 Synchronous blocking I/O One slow tool freezes all others Use async def and connection pooling

4 No input validation Garbage inputs crash the server Use Pydantic models for every tool schema

5 No tool-level error handling AI gets raw stack traces Wrap tools, return structured errors

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