An API is written for a developer’s code. MCP is written for an AI model that chooses its tools at run time — and it usually calls your API underneath.
The Model Context Protocol gives AI assistants and agents one standard way to find and use tools and data. This guide shows where it sits in your stack, how a call works step by step, what it adds for security, and how to decide which one a system needs.
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An API is a fixed set of endpoints a developer writes code against. MCP — the Model Context Protocol — is a standard an AI model uses to discover tools at run time, read their descriptions and decide which to call. In MCP vs API, they are layers rather than rivals: most MCP servers wrap an existing API, translating what a model asks for into the calls that API already accepts.
The clearest way to see the difference is to ask who sits on each end of the call, and what each side knows before the call is made.
A contract a developer reads, then codes against. The calling code knows in advance which endpoint it needs.
A protocol an AI host speaks to a server. The model learns at run time which tools exist and picks one for the task in front of it.
tools/list, read by the modelEvery MCP exchange follows the same few messages. The model never needs to know an endpoint in advance — only what the server lists. Under the current revision each request stands alone, so any copy of a server can answer it.
Optionally, the client asks which protocol versions and capabilities the server supports. Every later request carries its own version anyway.
The server answers with each tool’s name, description and input schema — and may list only what the caller’s token allows.
The model reads the descriptions against the user’s request and picks a tool and its arguments.
The host shows the call to the person before it runs: the specification says hosts must obtain explicit consent before invoking any tool.
The server runs the tool — typically by calling your API — and returns the result. If it needs more from the person, it says so and the client retries with the answer.
{
"tools": [{
"name": "find_invoices",
"description": "Find supplier invoices by
supplier, status or due date.",
"inputSchema": {
"type": "object",
"properties": {
"supplier": { "type": "string" },
"status": { "type": "string" }
}
}
}]
}Two standard transports. stdio: the client launches the server as a local process and they exchange messages over its standard streams. Streamable HTTP: each message is a POST to one endpoint — the form a company deploys for many users.
MCP is dated by revision, and the revisions matter: an article or SDK written for an early one describes sessions, a different sign-in flow and features that are now deprecated. Check which revision a server and a client speak.
The question that settles it is who makes the call. If your own code always knows what to call, an API is the right tool. If a model has to work out what to call from a person’s request, it needs MCP.
Who decides which operation runs?
An API trusts the code that calls it. An MCP server is called by a model acting for a person, so the questions change: whose permission is the model using, did the person agree, and can a tool description be trusted? The specification answers some of these with MUSTs and leaves the rest to the host and server.
Botminds offers both over the same platform: a hosted MCP server for people working through an assistant, and the Runtime API for services and scheduled jobs. Piper, the platform’s agents and outside assistants share one catalogue of tools, behind the same checks.
Read the full design in MCP for the enterprise, and how agents use tools in the agent harness.
No. In MCP vs API, MCP is a layer over APIs: most MCP servers call an existing API underneath. The API stays the contract for code; MCP makes the same capabilities usable by AI models that pick tools at run time.
It is a different shape. REST exposes resources over HTTP methods and URLs; MCP sends JSON-RPC 2.0 messages to one endpoint, where the client discovers tools, resources and prompts at run time, and a server can reply that it needs more input from the person before it finishes a call.
Anthropic introduced MCP as an open standard in November 2024, with the specification and SDKs open source. In December 2025 Anthropic donated it to the Agentic AI Foundation, a directed fund under the Linux Foundation co-founded with Block and OpenAI.
An OpenAPI description tells developers what an API offers. MCP gives an AI host a live list of tools with descriptions written for a model, a consent step before each call and a standard way to sign in. An OpenAPI description is a common starting point for MCP tools, but descriptions written for developers usually need rewriting so a model can choose between tools correctly.
Over HTTP, MCP uses OAuth 2.1. The server publishes Protected Resource Metadata (RFC 9728) naming its authorization server; the client registers — preferably with a Client ID Metadata Document — and sends the person through sign-in with PKCE; every call then carries a token issued for that server. Authorization is optional in the specification, and local servers over stdio take credentials from the environment.
Tools are functions the model may call. Resources are context and data to read. Prompts are templated messages a person can choose. A server offers any of the three; a host decides how to present them.
Yes. People connect their assistant to the Botminds MCP server and sign in as themselves, so every call runs within their role. Services, schedules and back-office jobs use the Runtime API with keys.
Bring one process and its documents. We’ll show you the agents, the controls and the record, running on your data.