MCP & AI TOOLS
MCP vs REST API: what's the difference?
A REST API is for programs written in advance. MCP is for AI clients that decide at runtime which tool to call. Most MCP servers sit on top of an API; it is not either/or.
What REST is designed for
A REST API is a contract between two programs. The endpoints are fixed, the payloads are documented, and a developer reads that documentation once and writes code that calls POST /search/query/products with the right body in the right place in the application. The call is the same every time it runs. That is the strength: deterministic, fast, cacheable, testable, and understood by every tool in the deployment pipeline. The product search page, the mobile app, the nightly integration with the warehouse system - all programs written in advance, all calling the contract they were coded against.
What changes when the caller is a model
Nobody is writing the integration at call time. The model is handed a question it has never seen and has to decide, there and then, what to call. It cannot read documentation in the developer's sense; it needs the options in front of it, described in language, with the argument shapes spelled out. It needs to be able to look at a result and decide what to do next, which may be another call. And it is in a conversation, not executing a request: the next question depends on this answer, and the context carries over.
MCP (Model Context Protocol) is a contract shaped for that caller. The server publishes tools with descriptions and schemas; the client fetches them at connection time, so the model always sees what is currently available; a call is a tool name with arguments, and the result is structured content the model can reason over. Discovery, description and structured results are the three things REST assumes a developer will handle, and MCP has to supply because there is no developer in the loop.
The error path shows the difference most clearly. A REST client gets a 400 and branches on it, because a developer wrote the branch. A model gets a message - unknown field: region_name; did you mean region? - reads it, and tries again with the right field, because reading and reacting is what it does. A good MCP server therefore writes its errors for a reader, not for a parser, and a good REST API does the opposite. Same failure, two audiences.
Volume is the other axis. A REST endpoint is called thousands of times a minute by code that never pauses; an MCP tool is called a few times per question by a model that reasons between calls. The first wants caching, rate limits and predictable latency. The second wants rich descriptions and results it can think about. Designing one contract to serve both callers well is harder than running two contracts over one engine.
Side by side
| Criterion | REST API | MCP server |
|---|---|---|
| Consumer | A program, written in advance | A model, deciding at runtime |
| Discovery | None at call timeA developer reads the docs | Built inThe client fetches the tool list |
| Contract | Endpoints, methods, payload formats | Tool names, descriptions, argument schemas |
| Who writes the integration | Your developers, once per client | Nobody per client; the model chooses tools from their descriptions |
| Auth | API keys, tokens, OAuth | An access key on the endpoint, issued by the server's owner |
| Error handling | Status codes the program branches on | Messages the model reads and reacts to, often by trying a different call |
| Where it runs | Your infrastructure | Your infrastructure, usually beside the API |
| Typical use | Product pages, integrations, batch jobs, anything deterministic or high-volume | Assistants, agents, ad hoc questions, many different clients |
When REST is right
Your own applications, where the code that calls the API is yours and the calls are known in advance. Integrations between systems, where the same request runs on a schedule. Anything high-volume - a product search page serving thousands of requests a minute does not want a model choosing tools in the middle of each one. Anything that must be deterministic: the same input, the same call, the same result, every time.
When MCP is right
Assistants and agents, where the questions are not known in advance and the caller decides at runtime. Ad hoc questions from people, in whatever words they use. Many different clients - ChatGPT, Claude, Cursor, a company's own assistant - that should all reach the same data without an integration each. MCP is the answer to "how do we let the AI tools our team uses ask our data questions" in the same way REST is the answer to "how do our applications read it".
Using both
Most systems that need MCP already have an API, and the MCP server sits on top of it, or beside it, over the same data. One indexed layer underneath: the same collections, the same search, the same facets. The REST API serves the product search page, where a program built in advance asks for jaket with facets on brand and size and renders the 531 results. The MCP server serves the analyst in Cursor who asks, in a sentence, which region drove last week's orders, and gets North 395 back as a tool result. Same index, same numbers, two doors. It is not either/or; it is one engine with a contract for each kind of caller.
The practical consequence is that adding MCP to a system that already has an API is small work, because the hard part - the index, the collections, the search and the facets - already exists. The server publishes a handful of tools over it, each one a thin description of a call the API already answers, and the AI clients arrive without any of them needing an integration of their own. The product page does not change. The analyst gets a new door to the same room.