MCP vs API: what decides which one you need

AWS defines APIs as mechanisms that let two software components talk to each other using a set of definitions and protocols, and calls the interface a contract of service between two applications. The program that sends the request is the client and the one that answers is the server. AWS lists four common styles: SOAP, RPC, WebSocket and REST.

The MCP documentation defines the Model Context Protocol as an open-source standard for connecting AI applications to external systems. Its own analogy is a USB-C port: one agreed plug between an AI application and the data sources, tools and workflows it needs to reach.

So the two words sit at different levels. "API" names any programmatic interface, built for any caller. MCP is one specific protocol with a published specification, official SDKs and a fixed set of message types, and it is designed for one kind of caller: an AI application acting for a user.

MCP has three participants. The host is the AI application, such as Claude Desktop or Visual Studio Code. The host creates one MCP client for each MCP server it connects to, and each server is a program that provides context. A server can run locally over standard input and output (stdio) or remotely over Streamable HTTP.

Messages are JSON-RPC 2.0 on both transports. A client asks a server what it offers: the tools/list request returns each tool’s name, description and a JSON Schema for its inputs. The client then runs a tool with tools/call. Under the protocol version dated 2026-07-28, every request carries the protocol version and the client’s capabilities, which is why the documentation calls MCP stateless.

• Tools: functions the model can call, for example a database query or a call to another API. The documentation lists the model as the party that controls them.
• Resources: read-only context such as file contents or a database schema, controlled by the application.
• Prompts: reusable templates that a user invokes on purpose, for example from a slash command.

A support team wants an assistant that can look up a customer’s open tickets. The ticketing system already has a REST API with an endpoint that returns tickets by customer, protected by an API key. A developer can call that endpoint from a script, and the script keeps working for as long as the endpoint does not change.

To let an AI application use the same lookup, the developer writes a small MCP server with one tool, get_open_tickets, whose input schema asks for a customer ID. Inside the tool, the server calls the existing REST endpoint. Any MCP host the team uses can now list the tool, show its description to the model and call it, with no code written for that particular host.

The REST API did not go away. MCP added a layer that describes the API in a form a model can discover and call, and it moved the decision about when to call it from the developer’s script to the model. The host decides whether a person must approve each call.

A plain API call fits when the caller is ordinary code with a fixed sequence: a nightly sync, a webhook handler, a payment capture. The developer knows which endpoint runs and when, and a model would add cost and uncertainty for no gain.

An MCP server earns its place when an AI application has to choose among actions at run time, when several hosts (a chat client, an editor, an agent runtime) should share one integration, or when you want tool descriptions and input schemas to travel with the integration instead of being rewritten for each host.

APIs fail in familiar ways: a breaking change to an endpoint, an expired key, a rate limit. An MCP server inherits all of those from the APIs it wraps and adds its own. Because the model picks the tool, a vague tool description leads to the wrong call, and a tool that writes data can act on a misread request.

The MCP security guidance names further risks for server authors: token passthrough, where a server accepts tokens that were not issued to it (the guidance says servers MUST NOT do this), confused deputy attacks through OAuth proxies, and scopes that grant far more than a task needs. The server concepts page suggests hosts offer approval dialogs for individual tool calls and activity logs of every execution. Treat both as requirements for any tool that changes data.

Swfte Connect is an API: one OpenAI-compatible endpoint in front of many model providers, with your own provider keys, routing and fallback, budgets and audit events. Agents built in Studio can act as MCP clients and call tools on external MCP servers.

Swfte also runs an MCP gateway. Today it checks that each call carries an API key scoped to that MCP server and workspace, and it logs the workspace, the server, the JSON-RPC method, the status code and the latency. It does not record the calling person, the tool arguments or the result. Per-tool allow and deny rules and per-call rate limits are designed for, not shipped, and Swfte does not currently expose your own agents as an MCP server.

• MCP gateway: what is built and what is not
• Swfte Connect: the model API
• Swfte Studio: agents that call MCP tools

Facts about other vendors and about the terms on this page were read on the pages below on 7 October 2026. Vendor plans, names and menus change, so check the vendor’s page before you rely on a detail.