What is an MCP and what does it do in AI tools?

MCP stands for Model Context Protocol, an open standard that Anthropic started popularizing and that several AI assistants now support. The idea, stripped of marketing: instead of coding a different integration every time you want an AI to talk to Trello, a database or an image generator, you define an “MCP server” once, and any compatible assistant knows how to use it.

Before this existed, connecting an assistant to an external tool meant writing custom code: authentication, request formatting, error handling, all different for each service. With MCP, whoever maintains the server, whether that’s the service’s own company or someone in the community, handles that part once, and the assistant just needs to speak the protocol.

In practice, an MCP exposes a list of concrete actions to the assistant: read a Slack channel, create a Trello card, generate an image in a given style. The assistant decides when to use each one based on what you ask it, the same way you’d decide which button to click in that tool’s interface if you were using it by hand.

Not every MCP is equally mature. Some have been polished for months and run without surprises, and some are freshly published and fail over silly things like a badly explained quota limit or a response in a format you weren’t expecting. Before giving write access to an MCP on something sensitive, a real project board, an email account, it’s worth testing it first on something that doesn’t matter if it breaks.

If you’re working with an agency or a technical team that says it uses MCP, the question that actually matters isn’t what it is, it’s what it has access to and with what permissions: reading isn’t the same as writing, and an assistant with write access to a real tool can mess up just as much as an unsupervised new hire would.