What Is MCP, and Why Should You Care?

Before MCP, every AI tool integration was custom-built — a bespoke connector for every model, every app, every data source. The Model Context Protocol gives that connection a shared shape.

The problem it solves

Instead of writing a one-off integration for each pairing of model and tool, MCP defines a common protocol: a server exposes resources and actions, and any MCP-compatible client can use them. Build the connector once, use it everywhere.

What a server actually exposes

An MCP server can offer tools (actions the model can call), resources (data it can read), and prompts (reusable templates). In cohort projects, students typically start by wrapping an existing API as an MCP server — turning a REST endpoint into something a model can use directly.

Where this is going

As more products ship first-party MCP servers, "does it have an MCP integration" is becoming a real question teams ask before adopting a tool — the same way "does it have an API" used to be.