What MCP is, in one paragraph
The Model Context Protocol is an open standard, first published by Anthropic in November 2024, for connecting AI applications to outside tools and data. An MCP server describes what it can do (tools the model can call, resources it can read, prompt templates) in a machine-readable way, and any MCP client, such as Claude, Cursor, VS Code or ChatGPT, can connect to it without custom glue code. Messages are JSON-RPC 2.0, sent over standard input and output for a local server or over HTTP for a remote one.
The difference that matters
An API (REST, GraphQL, gRPC) assumes a developer on the other side. Someone reads the documentation, decides which endpoint to call, handles pagination and errors, and ships code. The API does not need to explain itself at runtime, because a person already did the understanding.
MCP assumes the caller is a model. The server has to explain itself: every tool comes with a name, a plain-language description and a JSON Schema for its inputs, and the client fetches that list when it connects. The model reads the descriptions and decides, in the middle of a conversation, which tool fits the request. Nobody wrote code for that particular call.
That is why MCP is sometimes called "USB-C for AI". The value is not a better way to call one service. It is that one client can use hundreds of services through the same socket, and one server works in every client.
How they fit together
In practice an MCP server usually wraps an existing API. The GitHub MCP server calls the GitHub REST and GraphQL APIs; a Postgres server runs SQL against a database; a Stripe server calls the Stripe API. The server decides which operations to expose as tools, words the descriptions so a model uses them well, and handles auth.
So MCP does not replace your API. If you publish an API, an MCP server is a second front door for AI agents, the way an SDK is a second front door for developers.
When to use which
Call the API directly when you know exactly what should happen: a checkout flow, a nightly sync, a webhook handler. Code is cheaper, faster and easier to test than a model choosing tools.
Use MCP when the choice of action depends on what a person asks: "find the open issues about billing and summarise them", "pull last week's orders and flag the refunds". The model needs to see the options and pick, and MCP is the standard way to show it the options.
Many teams do both: code paths for the predictable work, an MCP server for the assistant sitting next to it.
What changes for security
An API key in your own code does what your code says. The same key inside an MCP server does whatever the model decides to call. That is the real difference in risk. Before you connect a server, check which of its tools write (send, delete, pay, post), whether a remote endpoint requires auth or is open to anyone, and whether the tool descriptions contain instructions aimed at the model rather than at you.
Every server listed here is scanned for exactly those things, and the report shows the evidence.