AI coding agents kept running the wrong test command, editing generated files and reading our .env. The fix was repo setup, not better prompts: a short instruction file, deterministic commands, permission rules, formatting hooks and a checked-in MCP config.
Teams wanted AI assistants to answer questions about internal ticket and order data through the Model Context Protocol. A local stdio server is easy; a shared remote one that respects each user's permissions is not. Our design for authentication, authorization, read-only tools, limits and audits.
A step-by-step build of a small Model Context Protocol server in Python with tools, resources and prompts, then how we test it three ways: the MCP Inspector, a scripted client in pytest and a real AI client, before anyone on the team relies on it.
Our internal MCP server passed every test in the inspector but kept failing in the desktop client with an 'is not valid JSON' error. A startup banner and a debug print went to stdout, which is the protocol channel in stdio transport. Logging to stderr fixed it.