MCP server

An AI chat program can, on its own, only generate text. It knows nothing about your files, your calendar, or a company’s database. For it to access these, it needs a connection to the outside world. An MCP server is exactly such a connection: a small add-on program that provides access to a single data source or application. It adheres to the Model Context Protocol, MCP for short. This is an agreed set of rules for how an AI program and such an add-on program talk to each other. The company Anthropic published these rules at the end of 2024 and made them freely available.

One plug instead of a hundred special cables

Before MCP, every connection had to be built individually. If an AI assistant wanted to access a calendar, someone wrote custom code for it. For the company database, custom code had to be written all over again. And if you switched AI providers, the work started from scratch. With five assistants and twenty data sources, that would theoretically mean a hundred individual solutions.

MCP works like USB-C for devices. There is one plug shape, and everything that has it fits together. Anyone who builds an MCP server for their database can use it with any AI program that understands MCP. Instead of a hundred individual solutions, you need five plus twenty. That is the real benefit.

This is why the protocol has spread unusually fast. Within a year, OpenAI, Google, and Microsoft, among others, supported it in their products. This is appealing for companies because they can connect their internal systems once and then switch AI providers more freely afterward.

Offering tools, executing tasks

An MCP server first tells the AI program what it can do. These capabilities are called tools. A calendar server, for example, reports: search appointments, create appointment, delete appointment. Each tool comes with a short description in plain language and a specification of what data it needs.

When the user then asks a question, the AI model itself decides whether one of these tools can help. For “When is my next dentist appointment?”, it selects the search function and sends a corresponding request to the server. The server carries out the query in the calendar and sends the result back. The model then turns this into an answer in full sentences.

The division of roles matters here: the server does not think along. It is purely a go-between linking the model and the data source. The decision as to which tool makes sense and when always rests with the AI model. Another common misconception is that an MCP server has to be located somewhere on the internet. Very many of them simply run locally on one’s own computer.

From the developer console to a security risk

MCP servers are most commonly encountered in programming. Tools like Claude Code or Cursor use them to connect project files, version control, or a test server. Desktop apps from AI providers can also be extended this way. You enter the desired server in the settings, and the assistant gains new capabilities.

By now there are public directories with thousands of ready-made MCP servers. These include connections to Google Drive, Slack, GitHub, mapping services, or payment providers. Many of them come from volunteers and are available free of charge.

In the news, the term often comes up in connection with security. An MCP server receives real access rights, for instance to company data or to deleting files. A manipulated or poorly built server can therefore cause considerable damage. Security researchers particularly warn against servers from unknown sources. Anyone who integrates one should treat it like a program they are installing themselves.