Developing and Testing MCP Servers (boliv.substack.com)

This piece is a practical how-to and reflection on developing and testing MCP (Model Context Protocol) servers — the middleware that lets LLMs safely and reliably call external tools. The author recommends starting with FastMCP, a Python framework that abstracts MCP’s protocol so tools become simple annotated Python functions, letting you run a local server with a single command and focus on tool logic rather than protocol plumbing. They highlight the MCP flow differences from typical REST APIs (a client-to-server registry that creates session IDs used for tool listing, prompt listing, and tool calls), and advise working at the right abstraction level to reduce noise — e.g., using Postman as a full MCP client to debug tokens, middleware and tool execution locally. The big significance: MCP servers ground LLMs in factual tools and give teams control over agent capabilities, but introduce new testing and scaling challenges because LLM interactions interleave with server calls and add non-determinism. For production readiness the author stresses standard software practices (unit tests, CI/CD, SRP) plus targeted stress testing. They recount using LLMs (Gemini) to discover load-testing options and generate K6 scripts run in Docker to simulate realistic LLM-driven loads — demonstrating how LLMs can accelerate tooling choices and rapid iteration while leaving intent and validation to engineers.