Once you understand that AI agents work by using tools, an obvious problem appears. Every useful integration (connecting an AI to your files, your GitHub, your calendar, your database) has to be built. And historically, every AI app built every one of those connections itself, in its own way. MCP, the Model Context Protocol, is the standard that fixes that.
The problem: everyone reinvents the same plug #
Say you want AI tools to be able to read your company’s wiki. Without a standard, the situation is a mess:
- Every AI app has to write its own custom code to connect to the wiki.
- The wiki has to be integrated separately for each AI tool that wants to use it.
- Multiply that by every tool and every data source, and you get a combinatorial explosion of one-off, brittle integrations that all do roughly the same thing.
It is the situation the tech industry has hit many times: lots of devices, lots of connectors, no common standard, so nothing plugs into anything else without an adapter.
The fix: one common protocol #
MCP is an open standard for how AI applications talk to external tools and data. Instead of every app-to-tool pair needing custom glue, everyone agrees on one protocol. This is why people describe MCP as “USB-C for AI tools”: a single, standard connector, so any compliant AI app can plug into any compliant tool or data source.
Build your wiki connection once as an MCP server, and any AI app that speaks MCP can use it. Write your AI app to speak MCP once, and it can use any MCP server anyone has built. The messy grid of custom integrations collapses into a shared standard.
How it fits together #
MCP has two sides:
- MCP servers expose capabilities. A server wraps some tool or data source (a filesystem, a code repository, a database, an API) and offers it in the standard MCP format: here are the tools I provide, here are the resources you can read, here is how to call them.
- MCP clients are the AI applications that connect to those servers and make the capabilities available to the model. When the agent decides to use one of the server’s tools, the client speaks MCP to invoke it and returns the result.
From the model’s point of view, nothing changes. It still just calls a tool and gets a result back, exactly as described in the tool-use article. MCP is the plumbing underneath that delivers the tools to the model in a consistent way, no matter who built them.
Why it matters #
MCP is not a flashy capability you can see. It is infrastructure, and its value is the ecosystem it unlocks:
- Reusable integrations. Build a connector once and it works across every MCP-compatible app, instead of being locked to one product.
- A shared marketplace of tools. Because the format is standard, a whole ecosystem of ready-made MCP servers can exist for common tools, so you plug in an existing one rather than writing your own.
- Less lock-in. Your integrations are not tied to a single AI vendor. Switch the app, keep the connectors.
- Faster building. For anyone building an agent, MCP means you can reach for existing connectors instead of hand-writing every tool from scratch.
When you will run into it #
If you use AI apps, you may connect them to MCP servers to give them new abilities (access to your notes, your code, a specific service) without either side needing custom development. If you build with AI, MCP is increasingly the default way to give an agent its tools and data, because it saves you from reinventing integrations that already exist.
The one-line version: MCP is a standard way to plug tools and data into AI. It does not change what an agent does, it changes how easily the things an agent needs get connected to it. Boring on the surface, quietly important underneath, which is exactly what a good standard is.