MCP Connector

A chat program with artificial intelligence can, on its own, only generate text. It doesn’t know what’s in your calendar, and it can’t open a file on a company server. For it to be able to do that, it needs a connection to the outside world. An MCP connector is exactly such a connection: a small additional program that sits between the AI assistant and a data source. It tells the assistant which actions it offers, for example “read appointments” or “search file,” and carries them out on request. The abbreviation MCP stands for Model Context Protocol, an open agreement about what form these requests and responses must take.

One plug instead of a hundred special solutions

Before MCP, every integration had to be built individually. Anyone who wanted to connect an assistant to a calendar, a ticketing system, and a database had to program three different interfaces. If you later switched the AI model, the work often started over from scratch. With five assistants and twenty data sources, that would theoretically mean a hundred individual solutions.

The protocol turns this equation around. Every data source gets exactly one connector, every assistant understands the protocol. A hundred connections become five plus twenty. The analogy to a USB port is an obvious one: a hard drive doesn’t need its own port for every computer model, it just adheres to a shared standard.

What matters is the difference between capability and knowledge. A connector doesn’t make the model smarter, since it doesn’t change anything about what the model learned during training. It merely gives it access to current information and to tools with which it can change something.

What happens between assistant and data source

Technically, the whole thing consists of two sides. The client sits within the AI application, for instance in the chat window. The server is the connector itself and resides with the data source. When connecting, the server sends a list of what it offers: available tools, readable resources, prewritten text templates.

If a user now asks a question, the model decides whether one of these tools can help. If someone asks about free time slots, it calls the calendar tool. The connector fetches the data, returns it, and the model formulates an answer from it. So the model doesn’t write database commands itself, it merely fills in predefined requests.

A common misconception: the connector does not check whether an action makes sense. It does whatever it is authorized to do. That’s why permissions are strictly regulated, and sensitive steps, such as deleting data, are left for the user to confirm. Otherwise, a manipulated text in the inbox could trick the assistant into taking actions nobody wanted.

MCP in products and the news

The protocol was published and made open by the company Anthropic at the end of 2024. Within a few months, other major providers adopted it. Today, several widely used chat assistants and development environments support MCP connectors directly. Ready-made connectors are available for download for common services such as file storage, project management tools, or code platforms.

In everyday use, you usually don’t notice any of this. When an assistant in the office finds an invoice in the company system or writes a note into a task management program, there’s often a connector behind it. The term comes up in the news when companies announce that they are opening up their software to AI assistants. What’s at stake is the question of who will control access to corporate data in the future.

MCP should not be confused with a single provider’s plug-in marketplace. Plug-ins only work within the product they were built for. An MCP connector isn’t tied to any particular product and can be used with any assistant that understands the protocol.