MCP Explained: How AI Connects to the Tools You Already Use

Ask an AI assistant to explain earthquakes, and it can probably give you a useful answer. Now ask it to find earthquakes recorded last month and summarize their locations.

That second request needs something extra: access to an earthquake catalog. The assistant needs a way to ask for records and receive the results.

This is where the Model Context Protocol, usually shortened to MCP, comes in.

An AI application might need information from several places: files on your computer, a company database, a calendar, or a scientific data service. Each connection can require its own integration work.

MCP provides a shared standard for those connections. It describes how AI applications can discover available tools and exchange information with them. Developers still have to build and configure the connections, but they can use a common approach instead of designing every connection from scratch. The official introduction compares MCP to a USB-C port for AI applications.

Think about plugging accessories into a laptop. A shared connector makes it easier to use different devices. It does not decide what each device does. In a similar way, MCP standardizes communication while the connected tools do their particular jobs.

Anthropic, the company behind Claude, introduced and open-sourced MCP on November 25, 2024. Although it started at Anthropic, it is an open standard that other AI applications and tool developers can implement.

Imagine we build a read-only connection between an AI assistant and an earthquake catalog. We give it a tool that searches earthquakes by date and magnitude.

You ask:

“Find earthquakes of magnitude 5 or greater worldwide during September 2026.”

Here is what would happen:

The earthquake records come from the catalog. The assistant helps you ask for them and understand the result.

This is an illustrative integration, not a claim that USGS provides an official MCP server. The real USGS earthquake catalog service already supports searches using dates, magnitudes, and locations. A developer could build an MCP connection around that service.

In this context, a server is software that offers capabilities to the AI application. It could run on your own computer or on another machine. It does not necessarily mean buying or maintaining a large cloud server.

The useful distinction is between the application you interact with and the software that connects it to a particular tool or data source. The MCP architecture guide explains how those parts communicate.

You may have heard the term API. It means an interface that software uses to communicate with other software.

Our earthquake example can use both: the MCP server communicates with the AI application using MCP, then requests records through the catalog’s existing API. The catalog does not need to be rebuilt just to support this workflow.

Connecting an assistant to outside tools also means deciding what it should be allowed to do.

Searching public earthquake records is different from deleting files or sending an email. For this example, read-only access is enough. There is no reason to give the assistant permission to change the catalog.

MCP alone does not guarantee a secure connection or a correct answer. Developers still need appropriate permissions and safeguards, and readers should be able to check the sources behind a summary. The project publishes security guidance for implementations.

In our example, which part supplies the earthquake records: the AI model, MCP, or the catalog?

The catalog. MCP provides a standard way to connect the pieces, and the assistant turns the returned information into something useful.

That is the central idea: a common way for AI applications to work with information and tools beyond the conversation.