# MCP vs API: what's the difference?

MCP doesn't replace APIs — it sits on top of them. An API exposes a product's functionality to developers who write code against it; an [MCP server](https://mcpyet.com/what-is-an-mcp-server.md) re-exposes that same functionality to AI models in a self-describing format they can discover and call at runtime. Nearly every MCP server is a thin wrapper around the vendor's existing API.

## Side by side

| | API (REST, GraphQL…) | MCP |
|---|---|---|
| Built for | Developers writing code | AI models choosing actions at runtime |
| Discovery | A human reads the docs | The client asks `tools/list`, gets typed schemas back |
| Interface shape | Different per product — endpoints, verbs, conventions | Uniform — JSON-RPC 2.0 tools, same shape for every product |
| Integration cost | One integration per app-and-product pair | One server per product; every MCP client can use it |
| Handling change | Breaking changes mean code updates downstream | Clients re-discover the tool list every session |
| Auth | Per-API keys and OAuth flows, wired by each integrator | OAuth standardized in the protocol for remote servers |

## When to use which

- **Building software that talks to a product?** Use its API — MCP would add a layer you don't need, and APIs remain the contract with the best performance and control.
- **Giving an AI assistant access to a product?** Use its MCP server if one exists — check <https://mcpyet.com/browse.md> — because the discovery, schemas and auth come solved, and the same setup works in Claude, Cursor, ChatGPT and the rest.
- **Vendor deciding whether to ship one?** It's not either/or: the MCP server is a thin layer over the API you already run, and it's rapidly becoming the difference between assistants that can use your product and assistants that can only describe it.

## Common questions

**Does MCP replace REST APIs?**
No. Nearly every MCP server is a wrapper around an existing API — the API keeps doing the actual work. MCP replaces the per-app glue code between AI assistants and those APIs, not the APIs themselves.

**Is MCP itself an API?**
Technically yes — it's a JSON-RPC 2.0 interface over stdio or HTTP. The useful distinction is the consumer: a product API is designed for developers writing code against documentation; MCP is designed for AI models discovering capabilities at runtime.

**Can an AI assistant use a plain API without MCP?**
Yes, through custom function calling: you describe each endpoint to the model and write the execution code yourself. That works fine inside one app — but the work repeats for every app and every API, which is exactly the multiplication MCP exists to remove.

**If I already have an API, how much work is an MCP server?**
Small, as integrations go. Official SDKs exist for the major languages, and a basic server is a thin translation layer: one tool definition per capability you want to expose, delegating to the API you already run.

More pairings: <https://mcpyet.com/compare.md> · protocol basics: <https://mcpyet.com/what-is-mcp.md>

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Source: https://mcpyet.com/compare/mcp-vs-api/ — data refreshed 2026-08-11