Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard that connects AI models to external tools and data sources in a uniform way. Instead of building a custom integration for every connection, the model speaks a fixed language through MCP that every compatible tool understands. You can think of it as a universal plug between a large language model and the systems it needs to work with.

How does it work?

MCP sits between an AI application and the outside world. An MCP server exposes certain capabilities, for example access to a database, a file system or an API, and the model connects to it as a client. Because the conventions are fixed, any application that supports MCP can use that same server without new code. If you build an MCP connection for your CRM, both your AI agent and another AI tool can use it right away.

Why it matters for B2B

The gain is less integration work and less lock-in. Without a shared standard, you connect each AI system separately to each internal system, which quickly produces a tangle of fragile connections. With MCP you build a connection once and reuse it broadly. For a B2B organization, that means switching between AI tools faster and lower maintenance costs, so budget goes toward results rather than glue between systems.

The pitfall

A uniform connection also makes it easier to give a model access to more than is wise. Every MCP server you open is a door to your data, so permissions, authentication and logging need to be in order from the start. Treat MCP as a powerful but serious integration, not a toy you quickly hook up to production data.