In one sentence
MCP, the Model Context Protocol, is a standard way for AI systems to connect to tools and data sources, so the same connection can be reused across different AI applications.
The simple model
An AI often needs to reach outside itself: to read a document store, call a service, or use a tool. Historically each of those connections was built in its own way for each application. A standard like MCP defines a common way to describe and expose these connections, so a tool built once can be used by many AI applications.
Think of it like a standard plug and socket. Before standards, every device needed its own custom wiring. A shared standard means anything built to it can connect to anything else built to it.
Why you care
For most people designing a workflow, the important thing is not MCP itself but what it enables: connecting an AI to the specific tools and data your workflow needs, in a way that is reusable rather than one-off.
When you plan a workflow, the durable questions are the same as always: which tools and data must the AI reach, does each only read or also write, and what is it allowed to do. The connection standard is how those get wired up; it does not change what you should decide. See what is a tool and what is an integration.
A small example
A team wanted their assistant to reach a document store and a ticketing system. Rather than building two custom connections, they used a standard connector approach so each tool could be described once and reused. The design work that mattered, deciding the assistant could read documents but only draft tickets, was unchanged by the choice of standard.
What this does not solve
A connection standard makes wiring tools to AI easier and more reusable; it does not decide what the AI should be allowed to do, nor make those actions safe. Permissions, guardrails, validation and review still have to be designed around each connected tool.
