What Is MCP? APIs and Connectors in Plain English

MCP, short for Model Context Protocol, is an open standard that lets an AI model connect to your other software, such as your calendar, your accounting system, or a folder of files, so it can read from them and sometimes act in them. In the plainest terms, an API is the doorway a program opens so other software can talk to it, a connector is a hallway someone already built through that doorway so you never touch code, and MCP is the standard plug that lets any AI tool use the hallway. The official MCP documentation reaches for the same kind of picture and calls it a USB-C port for AI applications.

What is MCP, and who controls it?

Anthropic, the company behind Claude, published MCP in late 2024 as a way to stop every AI app from needing a custom wiring job for every piece of software. In December 2025 it handed the protocol to the Agentic AI Foundation, a fund under the Linux Foundation that Anthropic, OpenAI, and Block co-founded with backing from Google, Microsoft, and others. So no single vendor owns the plug anymore, which matters if you're wary of building your habits on one company's goodwill.

Support is broad. Claude and ChatGPT both connect through it, and the same foundation announcement names Google's Gemini and Microsoft's Copilot among the products that have adopted it. The practical upshot is that a software company can build one MCP server for its product and have it work across the AI apps its customers already pay for, instead of building five separate integrations and maintaining all of them.

An MCP server, the phrase people search for right after "what is MCP," is the piece on the far side of the plug. It is a small program sitting in front of one system that tells the AI model, in a shared format, what it can look up and what it can do there. The AI app you type into is the client. Your calendar provider might run one server, your note-taking app another, and the AI app talks to both the same way.

MCP vs. API: What's the difference?

The doorway picture shows how they fit together. An API (application programming interface) is a set of rules a program publishes so other programs can ask it for things, such as "give me tomorrow's appointments" or "add this invoice." Most software a small business pays for has one, sometimes only on higher plans. An API is written for programmers, though, and every one is shaped a little differently.

MCP sits on top. An MCP server usually works by calling the API underneath, then translating it into a format any AI model can understand without a programmer writing custom code for each pairing. If the API is the door, MCP is a standard handle fitted to doors that were all built differently.

That gives you the question to ask before anyone sells you on connecting AI to your business: Does my tool actually open that door? A scheduling app with no API has no door, and no plug will fix that. One with an API but no connector has a door and no hallway, which is a real job for a developer. If the vendor publishes an MCP connector, setup is usually a few minutes in settings.

Here are the terms side by side.

What is a CLI, and why do AI tools use them?

A CLI (command-line interface) is a program you run by typing commands instead of clicking buttons. It's the black window with the blinking cursor that people close as fast as they can. Under the friendly screens, a lot of software can also be driven this way, and a CLI is really another doorway into the same system the API opens.

AI tools have picked them up for a boring reason, which is that typed commands are text, and text is what models are built to read and write. A coding tool can run a command, read the result, and decide its next step without anyone designing a special connection. For an owner, a CLI matters mostly as a sign of how much you could automate later. You won't use one directly unless you want to.

How do I check whether my software has a door?

Two searches answer it. Open your AI app's connector directory and look for the software you use. If it's listed, the vendor or a known developer has built the hallway, and setup is a sign-in. If it isn't, search the software's name plus "API" and see whether a developer page exists. A page with documentation means there's a door and someone could build the hallway. No page usually means no door, and nothing on the AI side can add one.

When the door exists and the hallway doesn't, that's the point where connecting your systems becomes a project worth paying for. Why connecting them is worth doing at all, and what changes when your tools stop living apart, is the subject of what data integration does for a small business. What to let the AI do once it's through the door, read only or read and write, is a separate decision, and how much access to give an AI agent takes it one step at a time.

A connection that reads is Level 3 in the series' four levels. One that also takes actions on its own, in a loop, is what people mean by an AI agent, and the plug is the same in both cases. Which one you end up with is set by the permissions you grant.

Pricing is the part to watch. Some vendors already put API access behind their top plan, and more may follow as customers want their AI models walking through those doors. Before you pick a scheduling app or a point-of-sale system next time, ask which plan opens the API.