What is Model Context Protocol?

The idea

An AI model on its own only knows what it was trained on plus what you paste into the chat. MCP removes the pasting. It defines a standard way for the model to call out to a server that exposes real capabilities: read this file, run this query, fetch this site's traffic. The model discovers what a server offers and uses it, without a custom integration for each one.

Think of it as a universal adapter. Before MCP, every tool needed its own bespoke plug into every AI client. MCP is the shared shape both sides agree on, so any compliant client (Claude, Cursor, and others) can use any compliant server.

Why it appeared

Agents got good at reasoning faster than they got hands. For a while they could describe what to do but could not do it, because reaching your systems meant a one-off integration every time. MCP standardized that reach. Once the protocol existed, servers appeared quickly for code, files, databases, design tools, and analytics.

What a server actually is

An MCP server is a small program that speaks the protocol and wraps one source of truth. The GitHub server wraps your repositories. A Postgres server wraps a database. A website server wraps your site's live signals. You connect the servers you care about, and the agent treats them as tools it can call mid-conversation. Most people run a handful, not dozens.

One thing you can do today

The clearest first win is your own website. Connect a site server and your agent stops guessing about your traffic and starts reading it: which pages get visits, where conversions happen, what is broken right now. The next pages cover which servers do that and how to wire one up.