A traditional API is a fixed set of endpoints: you code which URLs to call, what parameters to send, and how to parse responses. Adding a new capability usually means new endpoint or new fields and then updating every client. MCP is built for LLM-driven tools: the client discovers at runtime what tools and resources a server offers (name, description, parameters). The server can add or change tools without the client’s code changing. You get a single protocol and a consistent shape (tools + resources) that any LLM app can consume.
MCP vs traditional API
| Aspect | Traditional API | MCP |
|---|---|---|
| Discovery | You code which endpoints to call; static. | Client asks the server what tools/resources exist; dynamic list at runtime. |
| Integration | Each app writes its own client for each API (auth, request shape, errors). | One MCP client talks to any MCP server; same protocol, same shape. |
| Change | New endpoint or parameter often means updating app code and redeploying. | Server adds a new tool; clients discover it on next connect; no app code change. |
| LLM use | You manually map API endpoints to tool definitions for the LLM. | Server advertises tools (name, description, params); client passes them to the LLM as-is. |
Example: When to use which
Use an API when you have a fixed, app-to-app integration and don’t need dynamic tool discovery (e.g. your backend calling a payment provider). Use MCP when you want AI apps and agents to discover and use capabilities at runtime — files, search, DB, your custom tools — without hardcoding each integration.