MCP vs REST API: what's the difference and when to use each

A REST API is a set of HTTP endpoints that your own code calls, written against that one service's docs. MCP, the Model Context Protocol, is a standard for AI applications: an MCP server lists its tools with JSON Schema inputs, and any MCP client can discover and call them at runtime. An MCP tool can call an API underneath, so the real choice is who drives each call: your code, or an AI model.

The MCP side comes from the MCP introduction, architecture, and server concepts pages and the 2025-11-25 tools and transports specification. The worked example is Sume's hosted MCP server and Developer API, from MCP tools and gates, the Image API docs, and the server's current code. All were read on 2026-09-28.

What is the difference between an API and MCP?

A REST API is built for a developer who reads its docs and writes code against it. MCP is built for a program that learns what a server offers while it runs. In the MCP docs' terms, the host is the AI application, such as Claude Code, and it creates one MCP client for each MCP server it connects to. How MCP relates to a model's own tool calls is a separate question, covered in MCP vs function calling.

Is an MCP server just a wrapper around an API?

It can be, but nothing requires it. The MCP docs say tools can write to databases, call external APIs, modify files, or trigger other logic, and that MCP covers only the protocol for exchanging context, not how the AI application uses the model. So an MCP server can sit in front of an API that already exists and add what a model needs: a named tool, a description, and an input schema.

Sume's hosted MCP server is one example: its docs say the tools wrap selected public API capabilities and are not full parity with the HTTP API (which Sume interface covers what).

What does the same call look like over REST and over MCP?

Here is one image request both ways on Sume. Over REST, your code sends POST /v1/images with a model, which the docs mark as required, and a prompt; model: "sume/auto" lets Sume pick the family. The call blocks for up to 30 seconds and returns the images, or returns a job with 202 if generation runs longer.

Over MCP, the AI client sends tools/call for generate_image. Sume's MCP docs say to omit payload.model to route to sume/auto, and every write or paid tool requires an idempotency_key. In current code the tool submits asynchronously by default and answers with a job id, which the agent then waits on with jobs_wait.

# REST: your code calls the endpoint
curl -X POST https://api.sume.com/v1/images \
  -H "Authorization: Bearer $SUME_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "sume/auto", "prompt": "a red panda astronaut, studio lighting"}'

# MCP: the AI client sends this JSON-RPC message to https://mcp.sume.com/mcp
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "generate_image",
    "arguments": {
      "idempotency_key": "panda-001",
      "payload": { "prompt": "a red panda astronaut, studio lighting" }
    }
  }
}

When should I use MCP instead of calling the API?

Pick by who decides what happens next. The MCP site's case for the protocol is that it reduces development time and complexity when building or integrating with an AI application, and that it makes it easy to build once and integrate everywhere. Both benefits assume an AI application is making the calls.

• Call the API when your code decides: a backend, a script, a queue worker. You control each request, its retries, and its error handling. Sume's basics page names api.sume.com as its public Developer API and says hosted MCP still works but is not part of the primary path today; MCP vs CLI vs API for AI agents maps each kind of agent to a Sume interface.
• Connect an MCP server when an AI application decides which call to make. The MCP site lists Claude, ChatGPT, Visual Studio Code, and Cursor among the clients that support MCP.
• Use both when code and AI clients need the same service: code calls the API, and AI clients reach the same capabilities through an MCP server that wraps it.
• Keep a person in the loop for calls that change or spend something. The MCP tools spec says there SHOULD always be a human in the loop with the ability to deny tool invocations.