MCP or API: what is the difference?
An API lets software talk to software. MCP gives an AI model access to tools and data, usually on top of that API. When do you need which one?
An API is how software talks to software. It's a fixed set of agreements that lets one program request data from another, or pass data to it. MCP is a standard way to give an AI model access to tools and data. You usually build an MCP server on top of an API that already exists, so you often end up with both.
What is an API?
API stands for application programming interface. Your accounting package and your web shop often have one. Through that API, other software can request a customer or fetch an invoice, for example.
Who decides which call happens? The developer. They write code that makes a fixed call at a fixed moment. Every night, yesterday's orders go to the accounting package, for example. And every API has its own conventions, so for each connection you read that one system's documentation.
What is MCP?
MCP stands for Model Context Protocol. It's an open standard for connecting AI applications to other systems. Claude and ChatGPT support it, and so do development tools like Visual Studio Code and Cursor.
An MCP server offers an AI model a few tools. Each tool has a name, a description in plain language and a definition of the input it expects. The model first asks which tools exist. When it gets a question, it picks the tool that fits and calls it. According to the specification, there should always be a person who can refuse such a call.
So the difference is in who chooses. With an API, the developer chooses up front, in code. With MCP, the model chooses in the moment, based on the question.
How do they fit together?
An MCP server usually just talks to your existing API. Say you have a tool "show open orders". Behind that tool sits a call to your web shop's API. That API simply stays. MCP adds a layer on top that an AI model can read and use.
What does that layer add? Descriptions the model understands, and one fixed way to find and call tools. That way is the same for every server. The layer is also where you decide what the AI may do. A tool you don't offer is a tool the model can't use.
When is an API enough?
When there's no AI involved. Two systems that exchange the same data every night are fine with an API. So is a dashboard that always shows the same figures.
And if your AI tool already has its own connection to your package that does what you want, there's nothing to build. Look at that first.
When does MCP add something?
When you want an AI assistant to decide for itself what to fetch. Your colleagues then ask questions in plain language, and every question is a little different. "Which of this week's orders haven't shipped yet?" Or: "When did this customer last order?" Fixing in code up front which call belongs to which question doesn't work then.
MCP also helps when you want to use the same connection from several AI applications. You build the server once. Every application that supports MCP can work with it after that.
The difference side by side
| API | MCP | |
|---|---|---|
| What for | Software talking to software | An AI model using tools and data |
| Who picks the call | The developer, up front in code | The model, based on the question |
| Description | Documentation for developers | Name, description and input per tool, read by the model |
| Conventions | Every API has its own | One protocol for every MCP server |
| In your software | Often already there | Usually a layer on top of your API |
What does this mean for you?
Does your system have an API? Then an MCP server can go on top of it. How that works in practice is in MCP: how to connect AI to the software you already use. Would you rather work AI into your own application? Then AI in your app or web app may fit better.
We build MCP servers on existing systems. An MCP integration costs €5,000 to €15,000 excl. VAT with us. What's involved is on MCP server development. Not sure whether you need MCP, or whether your API is enough? Message Adem.
Frequently asked questions
Does MCP replace my existing API?
No. An MCP server usually calls your existing API. That API keeps working for the connections you already have. MCP sits on top of it as a layer, for AI.
Do I need an API to use MCP?
Usually you build an MCP server on top of an API. A server that talks to a database directly is possible too. If your system has neither, there first has to be a way to reach the data.
Does MCP only work with Claude?
No. MCP is an open standard. Besides Claude, ChatGPT and development tools like Visual Studio Code and Cursor support it. How you add your own MCP server differs per application.