An MCP server for GitHub, under the hood, calls the same GitHub REST API a developer would. That one fact settles most of the “MCP vs API” debate before it starts: one of these runs on top of the other. They are not two answers to the same question. They sit at different layers of the stack.
What an API is
An API (application programming interface) is a defined contract between two pieces of software: an agreed set of endpoints, inputs, and outputs. It is bespoke per service. A developer reads that service’s documentation and wires the integration in advance, so the set of calls an application can make is fixed when the code is built.
Most web APIs follow the REST style, formalised in Roy Fielding’s 2000 dissertation. REST is stateless: each request “must contain all of the information necessary to understand the request, and cannot take advantage of any stored context on the server” (Fielding, 2000). The interface is uniform across resources, but what each endpoint means is specific to that one service. Learn the Stripe API and you have learned Stripe, not payments in general.
What MCP is
The Model Context Protocol (MCP) is an open protocol that standardises how an application built on a language model connects to external data and tools. A host (the AI application) runs clients that connect to servers, and each server exposes tools, resources, and prompts over JSON-RPC (MCP specification).
The move that makes MCP different is discovery. The model finds out what a server offers at runtime by sending a tools/list request, then invokes a capability with tools/call, instead of relying on calls a developer hard-coded ahead of time (MCP specification, tools). And because an MCP server frequently wraps an existing API, MCP does not stand opposite APIs. It layers a uniform, model-facing convention on top of them. For the protocol on its own terms, see what is MCP.
How they compare
The honest comparison names axes, not a winner, because the two things do not occupy the same slot.
| Axis | API (e.g. REST) | MCP |
|---|---|---|
| Layer | The raw interface to one service | A standard layer above interfaces; servers typically wrap existing APIs |
| Standardisation | A bespoke contract per service | One protocol (JSON-RPC) that every host and server shares |
| Discovery | A developer reads the docs; endpoints are known in advance | The client discovers tools at runtime via tools/list |
| Who wires it, and when | A developer, ahead of time, per integration | The agent, at runtime, against any server it connects to |
| Integration scaling | M×N (each app hand-built against each tool) | M+N (each app speaks MCP; each tool ships one server) |
| State | REST style is stateless; each request is self-contained | Stateful connections with capability negotiation |
| Primary consumer | Written for a developer to integrate | Designed for a model to discover and call |
The integration math
The clearest way to see why MCP exists is to count integrations. Without a shared protocol, connecting M AI applications to N tools means building M×N bespoke integrations: every application hand-wired to every tool, each one maintained separately. Ten apps and ten tools is a hundred connections.
MCP collapses that to M+N. Each application learns to speak MCP once, each tool ships one MCP server once, and any application can then reach any server. Ten apps and ten tools becomes twenty pieces to build, not a hundred. That reduction, rather than any new capability, is the practical reason the protocol caught on.
Runtime discovery versus a pre-wired contract
An API’s surface is known at design time; MCP’s is discovered at run time. With a plain API, the calls an application can make are the ones its developer coded against. With MCP, a client asks a server what it offers and gets the list back live, which means an AI agent can connect to a server it has never seen and learn what that server can do without a code change. A server can also tell connected clients when its available tools change, so the set an agent works with is not frozen at build time (MCP specification, tools).
Why “better” is the wrong question
Asking whether MCP is better than an API compares a floor to the building standing on it. Because MCP servers usually sit on top of APIs, the accurate statement is narrower than a verdict: MCP standardises how a model reaches APIs. APIs remain the substrate the software world already runs on, and every MCP server is, in the end, calling into that substrate.
So the choice is not either-or. An agent that needs one known service can call its API directly. MCP becomes worth the extra layer when a single application has to reach many tools it did not know about in advance, and the alternative is wiring each one by hand. Pick the layer that matches the problem, not the acronym that sounds newer.
Sources: Roy T. Fielding, Architectural Styles and the Design of Network-based Software Architectures (Ph.D. dissertation, University of California, Irvine, 2000, Chapter 5); Model Context Protocol specification, version 2025-11-25 and its tools reference.
Questions, answered
Is MCP a replacement for APIs?
No. An MCP server almost always wraps an existing API rather than replacing it. MCP standardises how a model reaches tools; the tool itself is still exposed through an API underneath. The two operate at different layers, so one runs on top of the other.
Does an MCP server need an API underneath?
Usually yes. Most MCP servers are thin adapters over an existing service API: they translate the model-facing MCP calls into the calls that service already understands. A server can also expose local data or scripts directly, but wrapping an API is the common case.
Who controls MCP and is it open?
MCP is an open protocol. It originated at Anthropic in November 2024 and now runs under open governance, operated as a Series of LF Projects, LLC (part of the Linux Foundation), with a public steering group and a proposal process for changes.
Can I still use a plain API with an AI agent?
Yes. An agent can call an ordinary API directly, with no MCP involved. MCP is optional standardisation, not a requirement. It earns its place when one application needs to reach many different tools without hand-building an integration for each one.
Brief is a team of AI associates you direct in plain language. Opening to a small group at a time.
Request access