MCP vs API: What's the Difference?
An API is a contract between two pieces of software, invoked by code someone wrote in advance. MCP is a protocol that lets a model discover the available tools at runtime and choose one, without anyone writing per-tool integration code. MCP does not replace APIs, it wraps them so a model can use them.
The two are not alternatives. MCP is a layer above APIs that makes them usable by a model that decides at runtime what to call.
| Criteria | MCP | A direct API |
|---|---|---|
| Who decides what to call | The model, at runtime | Your code, written in advance |
| Discovery | Tools listed by the server | Read the docs, write a client |
| Adding a capability | Register a server | Write and deploy new code |
| Reuse across AI clients | Same server, every client | Reimplement per client |
| Best for | Agent tool use | Deterministic backend calls |
What is an API in this context?
An API is a contract: named endpoints, typed inputs, defined responses. To use one you read its documentation, write a client, and deploy that client. The set of calls your software can make is fixed when you ship it.
That model is correct and efficient when your code knows what it needs. It breaks down when the caller is a language model that will decide, mid-conversation, that it needs to search Slack.
What is MCP?
The Model Context Protocol is an open standard for exposing tools to a model. An MCP server advertises the tools it offers, each with a name, a description, and a typed input schema. A client connects, asks what is available, and passes those descriptions to the model. The model picks one and the client invokes it.
The shift is that integration moves out of your application. The server describes itself, and any MCP-compatible client can use it without code written specifically for it.
How is MCP different from function calling?
Function calling is the model capability of emitting a structured call against a schema you supply. MCP is the standard for where those schemas come from and how the call is executed.
Without MCP, you define each function in your application and maintain it there. With MCP, the tool lives in a server outside your application, and every client that speaks the protocol gets it. They work together: MCP supplies the tool definitions that function calling consumes.
When should you use each?
Use a direct API client when the call path is deterministic. A nightly job that syncs records has no reason to route through a protocol built for runtime choice.
Use MCP when the model chooses the tool, when several AI clients need the same integration, or when you want to add capabilities without shipping application code.
Most production systems run both: MCP for what agents reach for, direct clients for the paths your own code owns.
What does MCP not solve?
This is the part most comparisons omit. MCP standardizes discovery and invocation. It says nothing about:
- Whose credentials the call runs under. The protocol has no opinion on per-user OAuth or token refresh.
- Who is allowed to call what. There is no permission model in the spec.
- Where the record goes. There is no audit requirement.
- Where servers run. Hosting, isolation, and networking are yours.
Those four gaps are the reason MCP gateways exist. If you are running MCP for one developer, you will not notice them. Running it for a company, they are the whole problem.
How does Metorial relate to MCP?
Metorial is MCP infrastructure rather than another protocol. It runs the servers, handles per-user OAuth so an agent acts as the person it is working for, applies access control to decide which tools each user reaches, and records every call in Tracing. The catalog is 1,000+ integrations, and custom or remote servers register alongside them.
Where a direct API still wins: a single integration your team already maintains, with one fixed call path and no plans to add more. In that case MCP is machinery you do not need yet.
Frequently asked questions
Does MCP replace REST APIs?
No. Almost every MCP server calls a REST API underneath. MCP adds a discovery and invocation layer a model can use, so the same API becomes callable without writing a client for it. The API stays the system of record.
Is MCP just function calling with extra steps?
Function calling is a model capability: the model emits a structured call. MCP is the transport and discovery standard around it, so tools live outside your application and any MCP-compatible client can use them. They compose rather than compete.
Is MCP slower than calling an API directly?
Slightly, since the call passes through a server that then calls the API. In practice the overhead is small next to model inference and the upstream request, and it buys runtime discovery and one integration surface across every client.
When should I call an API directly instead of using MCP?
When the call path is fixed and your code, not a model, decides what to invoke. Deterministic backend work is better served by a normal client. Use MCP when the model chooses the tool at runtime or when several AI clients need the same integration.
Can I expose my existing internal API over MCP?
Yes. Wrapping an internal API in an MCP server is the common path, and it means agents reach it through the same access control and logging as every other tool. Metorial supports custom and remote providers for exactly this.