MCP, short for Model Context Protocol, is an open standard for connecting AI models to the tools and data they need to do real work. Instead of writing a one-off bridge between each model and each system, you expose your tools through MCP once, and any model that speaks the protocol can use them.
Think of the USB-C port on a laptop. Before a common port, every device needed its own special cable and socket. Once everyone agreed on one shape, a single port could charge a phone, drive a monitor or read a drive. MCP is that shared port for AI, which is why it sits close to function calling and general tool use, the mechanisms by which a model takes action.
Under the hood, the maths is simple but telling. Connecting five models to five tools the old way means up to twenty-five separate integrations to build and maintain. With MCP each tool exposes one server and each model speaks one protocol, so you build ten things instead of twenty-five, and the gap only widens as you add more of either. The tools themselves live behind an MCP server, which a model discovers and calls at runtime.
The practical payoff is reuse and less lock-in. A connector you build for one AI assistant works with the next one that supports MCP, so you are not rewriting integrations every time a better model arrives. The caveat worth naming: an MCP server hands a model real access to your systems, so what it can read and change needs the same care you would give any API key.
At TopDevs we use MCP to connect a client’s AI assistant to their existing systems through one clean standard, so the setup keeps working as models change.
