The Model Context Protocol (MCP) is a shared standard for connecting AI assistants to the tools and data they need — your files, a database, a ticketing system, a design tool. Before a common protocol, every connection was a bespoke integration; MCP is the "USB-C for AI" idea, one plug that many tools and many models can agree on.
It matters because an AI is only as useful as what it can reach. A model that can read your codebase, query your issues, and open your docs through a standard interface becomes part of your actual workflow rather than a clever box off to the side. Under the hood, MCP is how a model's tool calling reaches the outside world in a consistent way.
Why it matters at your desk. This is squarely an engineer's concern today: tools like Cursor and Claude Projects use MCP-style connections so an assistant can act across your real environment instead of a copy-pasted snippet. As the standard spreads, the same plumbing will quietly power the agents that non-engineers rely on.
What to watch for: every connection you open is also a door. An MCP server hands a model access to real systems, so the questions are the unglamorous security ones — what can it read, what can it change, and who approved the connection — not just how much time it saves.