I'm a backend engineer (Java/Spring, Kubernetes, that world) moving toward AI engineering, and I wanted to actually ship something in the agent ecosystem rather than read about it. So I built and published a small Model Context Protocol (MCP) server. This is the first of a planned series that gets progressively harder; this one was deliberately trivial in scope, because the real goal was to close the full loop: build → publish to npm → list in the official MCP registry → get discovered.
The subject is intentionally boring: AEMET, Spain's national weather agency, has a free public API. No auth headaches beyond an API key, no legal grey area, nothing from my day job. A clean sandbox to learn the mechanics.
What I did not expect was that the "boring" API had the two most interesting engineering lessons of the whole exercise.
What MCP is, in two sentences
MCP is an open protocol that lets AI clients (Claude Desktop, IDE agents, etc.) call external tools through a standard interface. You write a server that exposes a few typed "tools"; any MCP-compatible client can then discover and invoke them.
