Model Context Protocol, explained for working engineers

MCP gets described in a lot of abstract language about agents and tools. Stripped down, it is a standard way for a model to discover what actions and data sources are available to it, without you hand writing a custom integration for every single one.

The practical benefit shows up when you are connecting an AI workflow to more than one system. Instead of bespoke glue code per integration, you get a consistent interface the model can query and call against, the same way a REST API gives a consistent interface for a browser or a mobile app.

It is not a replacement for your existing APIs. It is a layer on top that makes those APIs usable by an agent in a structured, predictable way, which matters a lot once you are letting a model take real actions instead of just answering questions.