MCP vs API: What Is the Difference, and When to Use Each
Founder and CEO, The Drive AI
TL;DR: An API is a contract a program is written against: fixed endpoints, documented by and for developers. MCP (the Model Context Protocol) is a standard way for an AI assistant to find out at runtime which tools a server offers, read their descriptions and call them. MCP does not replace APIs; most MCP servers are a thin layer over an API, shaped for a model instead of a programmer. Build against the API when your own code decides what to call. Use or build an MCP server when an assistant such as Claude or ChatGPT should decide.
Disclosure: The Drive AI publishes this guide and offers both: a REST API for document extraction at dev.thedrive.ai, and an MCP server for working with files. The examples below use both.
What is an API?
An application programming interface is a set of operations one program exposes for others to call: POST /v1/extract with a document, get JSON back. The caller is code a developer wrote, so the developer reads the documentation, picks the endpoints, handles authentication and errors, and decides the order of calls in advance. REST over HTTP with JSON is the common form; GraphQL and gRPC are others.
What is MCP?
The Model Context Protocol is an open standard, introduced by Anthropic in November 2024 and since adopted by OpenAI, Google, Microsoft and most assistant makers, for connecting AI applications to outside tools and data. An MCP server exposes three kinds of things: tools the model can call, resources it can read, and prompts it can use. The client (Claude, ChatGPT, Cursor, an agent you wrote) connects, asks the server what it offers, and gets back each tool's name, a plain-language description and an input schema. The model reads those descriptions and decides which tool to call and with what arguments. Messages are JSON-RPC 2.0, over standard input and output for a local server or over HTTP for a remote one, and remote servers use OAuth for sign-in. The specification is at modelcontextprotocol.io.
What is the difference between MCP and an API?
| API | MCP | |
|---|---|---|
| Who calls it | Code a developer wrote | A model, through an assistant or agent |
| How the caller learns what exists | Documentation, read in advance | The server lists its tools at runtime, with descriptions |
| Who decides what to call | The developer, at build time | The model, at run time, from the user's request |
| Shape of the interface | Endpoints sized for programs | Tools sized for one decision a model can make safely |
| Integration work | One integration per app per API | One server works in every MCP client |
| Errors | Status codes for code to handle | Messages a model can read and act on |
The last two rows are the practical difference. Before MCP, connecting an assistant to a service meant a custom integration for each pair; with MCP, a service writes one server and every client can use it. And because the caller is a model, a good MCP server explains itself in words: what a tool is for, when not to use it, what to do after an error.
Does MCP replace APIs?
No. An MCP server almost always calls an API underneath. What MCP adds is a layer designed for a model: fewer, larger tools that each correspond to a decision, descriptions written to be read, and guardrails for actions with consequences. A thin MCP server that mirrors every API endpoint one for one usually works badly, because the model has to orchestrate dozens of low-level calls the API's developers expected a programmer to sequence.
When should you use an API, and when MCP?
Use the API when your own software decides what happens: a nightly job that extracts every invoice in a bucket, a form that converts an uploaded file, a pipeline with fixed steps. You get predictable calls, exact control over cost, and no model in the loop. Extracting fields from documents through dev.thedrive.ai in a scheduled script is this kind of job.
Use MCP when a person asks an assistant in their own words and the assistant should work out the steps: "find last year's signed leases and rename them by tenant", "which invoices from Acme are still unpaid". The model needs to explore, read and decide, and the same request should work from Claude, ChatGPT or an agent framework without a separate integration for each. The Drive AI's MCP server for Google Drive, OneDrive, SharePoint and Dropbox is this kind: tools such as fs_search and fs_read to find and read, and changeset_propose and changeset_apply to change files.
Many products need both: an API for developers building fixed flows, and an MCP server for assistants acting on a person's behalf.
How does MCP handle actions that change data?
That is the part an API leaves to the caller and an MCP server has to design for, because the caller is a model that can be wrong. Good servers separate proposing a change from making it. In The Drive AI's server, changeset_propose returns a preview (counts, samples, conflicts) and changes nothing; changeset_apply makes it happen, and a large or destructive change comes back asking for the person's confirmation before it runs; changeset_undo reverses an applied change as a whole. Clients add their own layer: Claude and ChatGPT ask the user before calling a tool unless it has been allowed to run unsupervised.
Is MCP secure?
As secure as the server and the permissions you grant it. Remote MCP servers sign you in with OAuth (how MCP authentication works), so the assistant acts with your account's permissions and you can revoke access. The risks are the model's: a malicious or compromised server can return text that tries to steer the model, and a tool with broad write access can do damage on a bad instruction. Connect servers you trust, review what each tool may do, and prefer servers that preview changes and can undo them. The best MCP servers for Google Drive, OneDrive and Dropbox compares how ten of them handle this.
Frequently Asked Questions
Is MCP just an API for AI?
Close, but the difference matters. MCP is a protocol for how an assistant discovers and calls tools, so one server works in every MCP client. The tools themselves usually call an API. Think of MCP as the standard plug and the API as the wiring behind the socket.
Do I need an MCP server if my product already has an API?
Only if you want assistants to use your product on a person's behalf. A developer can integrate the API directly. For Claude, ChatGPT and agent frameworks to use it without custom code, publish an MCP server that wraps the API in a small set of well-described tools.
Can an MCP server call several APIs?
Yes. A server can combine any number of APIs behind one set of tools. That is often the point: one tool such as "search across my drives" can call Google Drive, OneDrive and Dropbox underneath and return one answer.
Is MCP only for Claude?
No. Anthropic introduced it, but it is an open standard used by ChatGPT, Gemini, Microsoft Copilot, Cursor, VS Code and most agent frameworks. A server written once works in all of them.
What is the difference between MCP and function calling?
Function calling is a model feature: you describe functions in your request and the model returns which to call. MCP is a protocol between applications: the tools live in a separate server, which any client can connect to and discover. MCP clients typically use the model's function calling to invoke MCP tools.
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