An API is an interface for software: developers read the documentation, write code against it, and ship an integration. An MCP server is an interface for AI: assistants like Claude connect to it, discover what it offers, and use it in conversation — with no integration code written per consumer. If the people on the other end are programmers, you need an API. If they are people talking to an AI assistant, you need an MCP server. Plenty of companies will end up with both, often with one wrapped around the other.
The same shape, a different reader
Structurally the two are cousins. Both sit on a network, accept requests and return answers. The difference is who is on the other end and what they need in order to succeed. An API assumes a developer with time: they read the docs, handle the errors, and update their code when you change something. An MCP server assumes an AI with none of that: every tool describes itself — name, purpose, inputs — in a form the assistant reads the moment it connects. The documentation is the interface.
Side by side
When you need an API
When other software must call yours predictably, at volume, under a strict contract: a mobile app hitting your backend, a partner syncing orders nightly, a billing job that runs at midnight. APIs are for machines executing plans a human wrote in advance. Nothing about MCP replaces that, and an assistant improvising against your billing system is nobody's idea of progress.
When you need an MCP server
When the consumer is a person working through an assistant. Your team asking Claude about internal policy. A customer asking about your product from inside their own tools rather than on your website. A partner's agent that should be able to check availability or open a ticket without a six-week integration project on their side. In those cases an API technically suffices and practically fails, because nobody on the other end is going to write code — the whole point of asking an assistant is that you didn't have to.
You probably don't have to choose
The neat part is that the two compose. On SuperCognit, an HTTP tool you attach to an agent becomes a callable MCP tool — which means an API you already run can be surfaced through an MCP server without new engineering. The server adds the parts an API lacks for this audience: self-describing tools, cited knowledge sitting alongside the actions, and OAuth 2.1 with per-workspace isolation handled for you.
Standing the server up doesn't need engineers either. Import your website — crawled into a cited knowledge base in about two minutes — upload documents, attach the tools, publish. The same build produces a chat agent for your site and an MCP server that works in Claude and any MCP client, private or public, on a custom domain if you want one. If the access is worth money, you can sell it per-seat, with subscriptions and invoicing built in.
A rule of thumb
Ask who has to succeed on the other end. If the answer is a developer with a deadline, build the API and write good docs. If the answer is an assistant with a user behind it, publish the MCP server. And if you already have the API, the MCP server is mostly a wrapper and an afternoon — which is a pleasant amount of effort for a second front door.