MCP vs API: When to Use Each

Short version: an MCP is an API designed for an AI model to discover and call without your help. Both move data over the wire. The difference is who's driving.

The difference in one paragraph

A normal API assumes a developer reads the docs, hardcodes the right endpoint, and ships an integration. An MCP assumes an LLM hits the server cold, lists the available tools, reads the descriptions, and decides which one to call to satisfy a user request. The contract is structured so the model's choice can be reasonable without a human in the loop.

Side by side

	REST/GraphQL API	MCP
Caller	A human-written program	An AI model
Discovery	Static docs (OpenAPI, GraphQL schema)	Runtime listing of tools, resources, prompts
Auth	API keys, OAuth, JWT	Same, plus per-tool scoping
Schema	OpenAPI / GraphQL SDL	JSON Schema per tool, with natural-language descriptions
Transport	HTTP	stdio (local) or HTTP+SSE (remote)
Versioning	URL or header	Capability negotiation at handshake
Best for	Apps, integrations, mobile	AI agents, copilots, IDE assistants

When to use a regular API

• Your caller is a deterministic program
• You need raw speed (millions of requests per second)
• You don't want a model in the loop at all

When to use an MCP

• You want Claude, ChatGPT, or Cursor to use your product directly
• Your tool surface is large enough that hardcoding function definitions is painful
• You want one integration that works across all major AI clients

When to use both

This is the realistic answer for most teams. Keep your REST API for your apps and partners. Build a thin MCP server on top of it that exposes the subset of operations you want AI to use. The MCP server becomes the AI-facing facade; the API stays the system of record.

PreMan lets you test both from the same workspace. Point it at your REST API to debug a 500. Point it at your MCP server to see what tool list a model would actually see. Same UI, same history, same auth.

→ Test your API and MCP side by side in PreMan