Building an MCP server in Python: what I learned about tool design

<p>When I built the MCP server for this project — it combines GitHub and DEV.to into a set of tools an agent can call — I had a decision to make before writing a single tool: talk

<p>The AI world has a plumbing problem. We have incredible language models, but connecting them to real tools — databases, APIs, file systems, other agents — still feels like

<p>MCP is one of the most thoughtfully scoped technical standards to come out of the AI ecosystem. That scope is intentional. Understanding exactly where MCP stops is the

<p>Most developers who build AI agents make the same mistake: they spend weeks designing the orchestration layer, tuning the system prompt, and picking the right model — then hand

<p>REST APIs gave humans (and the code they wrote) a standardized way to access software over the network.</p> <p>MCP is trying to do the same thing for agents.</p> <p>That simple

<p>Most API documentation is written for humans.</p> <p>MCP tool descriptions are different.</p> <p>They are read by the model that decides what to call next.</p> <p>That means

<p>Giving an LLM a database connection is one of those ideas that sounds great in a demo and terrifying in production. The agent writes a slightly-wrong query, and now you're

<p>You've built the demo. It works. The LLM responds, the tools fire, the output looks great.</p> <p>Then you push it to production — and everything breaks.</p> <p>API calls fail