What is MCP (Model Context Protocol)?

MCP, the Model Context Protocol, is an open standard for connecting AI models to external tools and data sources in a consistent way. Think of it as a universal adapter that lets an LLM plug into your files, apps, and databases without a custom integration for each one.

What problem does MCP solve?

The problem it addresses is messy plumbing. To be useful, an AI assistant often needs to reach real systems — your documents, a calendar, a code repository, a company database. Before a shared standard, every one of those connections had to be built bespoke, and each new tool meant another one-off integration.

MCP defines a common language so that any MCP-compatible tool can talk to any MCP-compatible AI, the way USB let any device plug into any port. Build the connection once, and it works everywhere that speaks the protocol.

How does MCP work?

In practice, MCP splits the setup into two simple roles:

• MCP server — wraps a data source or tool (a file system, a database, an app's API) and exposes what it can do in the standard format.
• MCP client — the AI application, which discovers the available servers and calls them on the model's behalf.

An assistant acting as a client can then securely fetch context or take actions across many systems through one consistent interface, instead of the developer wiring up each service by hand.

Why does MCP matter?

MCP has gained rapid adoption as AI assistants and agents move from answering questions to actually doing work across your tools — reading files, updating records, running tasks. That shift only works if the model can reach your systems reliably and safely, and a shared protocol is what makes those connections repeatable instead of one-off. It also gives you a single place to manage what the AI is allowed to touch.

If you hear about an AI that can connect to your apps, a protocol like MCP is increasingly what makes that possible under the hood.