MCP Connectors
MCP connectors are standardized interfaces that allow an AI assistant to uniformly access external tools and data sources – such as calendars, databases, or web browsers. They are based on the Model Context Protocol (MCP) and are meant to prevent developers from having to program a separate integration for every new combination of model and tool.
An AI assistant can inherently only process and output text. If it needs to send an email, query a calendar, or search a database, it needs a connection to the outside world. MCP connectors are exactly that: ready-made connecting pieces between an AI model and an external program or data store. The abbreviation MCP stands for Model Context Protocol, meaning a set of rules — a jointly agreed-upon format — that defines how these connections are built and used. Anthropic, the company behind the AI assistant Claude, released this protocol in 2024 and made it an open standard. Since then, it has been adopted by an increasing number of tools and platforms.
Why a unified protocol makes the difference
Without a common standard, every developer has to start from scratch for every new combination. A model that needs to interact with Google Calendar, Slack, and a company’s own database requires, without MCP, three completely different integrations. If a fourth tool is added, the work starts all over again.
MCP solves this problem similarly to a USB standard for hardware: instead of building a different plug for every device, everyone agrees on one shape — and anything that supports this plug works together. A developer writes the connector for their tool once, according to the MCP standard. After that, any model that understands MCP can use this tool immediately. The effort required on both sides drops dramatically.
This is especially attractive for companies: they can package their own internal systems as an MCP connector once and then open it up to multiple AI applications at the same time, without having to connect each application individually.
Structure and process of an MCP connection
An MCP connection always has two sides: a client and a server. The client is the AI model — more precisely, the application that uses the model. The server is the external tool that delivers data or performs actions. A connector is the concrete implementation of this server side for a specific tool.
When the model receives a task that requires external data, it sends a request in MCP format to the appropriate connector. The connector translates this request into the tool’s language — for example, a database command or a calendar API —, retrieves the result, and returns it in MCP format. The model receives the response and processes it further. To the user, this appears seamless.
The protocol not only defines the format of the messages but also which capabilities a connector can announce. The model first queries what a connector can do and then chooses the appropriate approach. This way, a single connector can offer multiple actions — for example, “read appointment” and “create appointment” in a calendar connector.
MCP connectors in products and the news
Since the release of MCP, a rapidly growing ecosystem has emerged. Developers worldwide have published connectors for commonly used tools: GitHub, Slack, Google Drive, Jira, PostgreSQL databases, and many more. Most of these connectors are publicly accessible and can be integrated into one’s own projects.
In practice, MCP connectors show up wherever AI assistants are meant to independently carry out tasks — so-called agents. An agent tasked with writing a travel report, who must independently fetch current flight prices, check the company calendar, and save the result as a document, needs exactly these kinds of connections. MCP makes it possible to build this chain as a single, seamless whole.
In tech reporting, the term comes up primarily in the context of enterprise AI and automation. Microsoft, OpenAI, and other major providers have announced plans to integrate MCP into their own platforms. This shows that, despite its short existence, the standard is already establishing itself as a shared foundation — similar to how HTTP established itself as the unified protocol for web pages in the early days of the internet.