I’ve been watching Model Context Protocol (MCP) servers pop up everywhere as the glue between AI agents and the real world. The pitch is simple: expose tools and data through a standard protocol and suddenly your AI agents can plan trips, analyze documents, query databases, or in my case, work with maps. MCP clicked for me because it’s opinionated where it matters and unopinionated where it shouldn’t. It standardizes how clients and servers talk, but it doesn’t box you into a single stack. Think of it as the USB-C of AI integrations: one cable, many devices.  

The first time I connected Visual Studio Code with GitHub Copilot, I expected party tricks. I got something closer to a new way of working. It wasn’t that code appeared by magic. It was that the little frictions, like the boilerplate, the glue code, the tests I knew I should write but kept postponing, stopped dominating my day. VS Code had already been my editor of choice for its ergonomics and extensibility; Copilot turned that editor into a cockpit where intention became motion. This post is my field report: what I actually run, how I prompt, where it saves time, and where I still slow down and think.

Imagine this: you work at a company, and you have a clear idea for a web app, something like a custom expense tracking tool that fits exactly the way your team works. You’ve tried off-the-shelf products, but they always miss the mark. The alternative, building your own application, is usually time-consuming, expensive, and requires getting developers involved early on. But what if you could prototype your idea, adjust the UI, change the data model, and publish it, all without writing a single line of code?