MCP vs REST API vs function calling: what's the difference?
REST powers apps, function calling powers a single model, and MCP makes tools portable across every AI client. We compare all three and show when to reach for each.
REST APIs, model function calling, and MCP are often confused because all three are about "letting software call other software." But they operate at different layers. REST is how applications talk to services. Function calling is how a single model invokes functions you define for it. MCP is how tools become portable across every AI client. This guide clears up the difference and shows when to use each.
- →REST API: a contract between your code and a service. The caller is a developer's program.
- →Function calling: a model emits a structured call to functions you wired into one app.
- →MCP: a standard so tools work across many AI clients without per-app integration.
- →They're complementary — an MCP server usually calls a REST API underneath.
- →Use MCP when you want your capability reusable by any AI assistant, not just one.
The quick comparison
Consumed by programs you write. Stable, ubiquitous, not AI-aware.
Model picks a function; you handle execution. Tied to a single app.
Open standard. Build a tool once; every MCP client can use it.
REST API: the foundation
A REST API exposes resources over HTTP — GET /users, POST /orders, and so on. It's designed for developers writing client code. REST knows nothing about AI; it just returns data. Crucially, REST doesn't disappear in an AI world — it's still the engine underneath. When an AI calls a tool, something eventually makes a REST request to your backend.
Function calling: one model, your functions
Function calling (sometimes called tool use) is a model capability: you give the model a list of function definitions, and instead of answering in prose, it can emit a structured request to call one of them. Your application code then executes that function and feeds the result back.
The catch: those function definitions live inside your application. If you also want the same capability in another AI app, you re-implement the wiring there too. That's the M×N integration problem.
MCP: making tools portable
MCP standardizes the layer above function calling. An MCP server advertises its tools in a common format, and any MCP-compatible client can discover and call them. You build the integration once; it works in Claude Desktop, in Cursor, in an IDE, and in custom agents. Under the hood, the client still uses the model's function-calling ability to decide which tool to invoke — MCP just makes the catalog of tools shared and reusable.
How they stack together
- A user asks an AI client to do something.
- The model uses function calling to choose an MCP tool.
- The MCP client sends the call to your MCP server.
- The server makes a REST request to your actual backend.
- Results flow back up the chain to the model.
So it isn't "MCP versus REST versus function calling" — it's all three working in concert, each at its own layer.
When to use which
- Use a REST API when programs (or MCP servers) need to talk to your service. You almost certainly already have one.
- Use function calling directly when you're building a single, closed AI app and don't need the tools anywhere else.
- Use MCP when you want your capability available across many AI clients, or you want to ship an official integration customers can connect from their own assistant.
The fastest path from REST to MCP
If you already have a REST API with an OpenAPI spec, you don't need to write function definitions or a server by hand. Cast converts the spec into MCP tools and hosts the server, so your existing REST endpoints become callable by any AI client through one URL.
Turn your API into an MCP server
Upload an OpenAPI spec, configure auth, and get a live MCP endpoint in minutes — no infrastructure to manage.
Try Cast freeFrequently asked questions
Does MCP replace REST APIs?
Is MCP just function calling with extra steps?
Can I use function calling without MCP?
Which is more secure?