Model Context Protocol
The Model Context Protocol (MCP) is an open agreement on how an AI assistant accesses external data sources and tools. Instead of building a separate connection for every application, the AI speaks the same language with all systems.
A chat program with artificial intelligence can only generate text on its own. It knows nothing about your files, your calendar, or your company’s database. In order to access those, it needs a connection to the outside world. The Model Context Protocol, or MCP for short, is a set of rules for exactly such connections. It defines how an AI asks for data and how the other program responds. The company Anthropic released MCP at the end of 2024 and made it openly accessible, so that anyone may use it.
Why connections used to be so expensive
Before MCP, every connection had to be programmed individually. If you wanted to connect five different AI programs to five different data sources, that could mean, in the worst case, 25 separate components. Experts call this an M-times-N problem. Every new data source multiplied the effort required.
With a shared protocol, this becomes an M-plus-N problem instead. Every AI application learns MCP once. Every data source offers MCP once. After that, everything fits together without anyone having planned the combination in advance.
The comparison that often comes up: MCP is the USB-C port for AI applications. In the past, every device had its own charging cable. Today, a single plug is enough for phone, laptop, and headphones. The benefit lies not in better technology, but in the fact that everyone agrees on the same format.
Server, client, and the three building blocks
MCP knows two roles. The client sits inside the AI application, for example in a chat program or a development environment. The server sits with the data source, for example the calendar, the file system, or the company database. Both exchange messages in a defined format.
A server offers three kinds of things. Resources are data for reading, such as the contents of a file. Tools are actions that change something, such as sending an email. Prompts are prefabricated instruction building blocks for recurring tasks.
What matters is the sequence: the language model decides on its own when it needs a tool. It sends a request to the server, gets a result back, and continues working with it. For actions that change something, the application usually asks the user for confirmation. This is also exactly where the greatest risk lies: a malicious server can try to slip harmful instructions to the AI. One should therefore only trust servers whose origin is known.
MCP in developer tools and in the news
Today, MCP is most commonly encountered in programming tools. Editors and AI assistants for software use it to access project files, version control, or bug databases. Services such as Google Drive, Slack, or database systems have also gained MCP servers.
MCP appears in business news when the topic is standards. OpenAI, Google, and Microsoft announced in 2025 that they would support the protocol as well. This is notable because a competitor made the proposal. Whoever sets a standard often determines what an entire market will look like later on.
MCP is frequently confused with the term AI agent. An agent is an AI system that independently carries out multiple work steps one after another. MCP is merely the conduit through which such an agent gains access to data and tools. The protocol therefore does not make the AI smarter. It only extends its access to the world outside the chat window.