An API lets one piece of software talk to another. MCP (Model Context Protocol) is a newer idea: a standard way for AI models, particularly large language models, to discover and use tools, files and data sources without a developer wiring up a custom integration for each one.
What an API actually is
API stands for application programming interface. It is a general term for any defined way that one program can request something from another: a weather service exposing endpoints for temperature data, a payments platform exposing endpoints for charging a card, a mobile app talking to its backend.
APIs come in many shapes (REST, GraphQL, gRPC, and so on), but they share a common pattern: the developer reads the documentation, learns the specific requests the API expects, and writes code to call it.
import requests
response = requests.get(
"https://example.com/api/weather",
params={"city": "London"}
)
print(response.json())That code only works because a human read the docs and wrote it to match this one API. A different API means different code.
Where MCP fits in
MCP is a protocol designed for a different problem: how does an AI model know what tools exist, what they do, and how to call them, without a developer hand-coding every possible connection in advance?
Instead of a model being limited to whatever a developer explicitly programmed it to call, an MCP server describes its own tools and data in a standard format. Any MCP-compatible client, including an AI assistant, can read that description and work out how to use the tool on the fly.
In practice, an MCP server is often just a wrapper around existing APIs or local resources: a database, a file system, a search tool. The MCP layer sits on top and explains, in a language the model understands, what is available and how to ask for it correctly.
The key difference
An API is built for a specific integration between two known systems. The developer on each end already knows what the other expects.
MCP is built for flexible, self-describing integrations, aimed at AI models that need to figure out at runtime what tools they have access to and how to use them, potentially across many different servers they have never seen before.
You could say MCP is not a replacement for APIs. It is a standard way of describing and exposing capabilities, many of which are still implemented using ordinary APIs underneath.
A simple way to picture it
Developer -> writes code -> calls a specific API
AI model -> reads MCP description -> decides which tool to call -> tool calls its own API internallyThe first flow needs a human to bridge the gap every time. The second flow lets the model do that bridging itself, provided the tool speaks MCP.
The common gotcha
People sometimes assume MCP replaces APIs entirely, or that every API needs to become an MCP server. Neither is quite right. MCP adds a discovery and description layer aimed at AI use cases; it does not remove the need for the underlying service to do actual work, which is usually still an API doing the heavy lifting behind the scenes.
Key takeaways
- An API is a general-purpose interface for one system to talk to another, built for a known integration.
- MCP is a protocol aimed at AI models, letting them discover and use tools and data sources in a standard, self-describing way.
- MCP servers often wrap existing APIs rather than replacing them.
- Think of MCP as a layer on top of APIs, not a competitor to the concept of an API itself.