Unlock AI with MCP
The Model Context Protocol (MCP) is how you wire an LLM up to the outside world. Left to itself, a model only knows its training data and whatever you've stuffed into the context. MCP lets it reach out and actually do things: hit APIs, query databases, poke at external systems for live data and control.
I think of it as HTTP for LLMs. It gives the model a standard way to find out what tools exist, through a bit of metadata describing each one. An AI sales agent might connect to an MCP server to pull customer orders from a database, with parameters like a date range narrowing down what comes back.
Each tool comes with a description and its parameters, so the model can decide to call it when the moment fits rather than waiting for you to pull a trigger. Tools aren't the whole story either. MCP servers also expose resources (snippets of context) and prompts (predefined templates).
The bit I like is that you can compose them. Run several MCP servers together and stitch them into a workflow: one fetches the data, another formats it into a report, a third drops the result into your CRM.
There's a healthy supply of ready-made servers out there if you don't fancy building your own. The official MCP servers repository and registry on GitHub is the place I'd start, and beyond that you've got the likes of Awesome MCP Servers, the Smithery Registry, and the Pulse directory.