However, as someone that has tried to use OpenAPI for that in the past (both via OpenAI's "Custom GPT"s and auto-converting OpenAPI specifications to a list of tools), in my experience almost every existing OpenAPI spec out there is insufficient as a basis for tool calling in one way or another:

- Largely insufficient documentation on the endpoints themselves

- REST is too open to interpretation, and without operationIds (which almost nobody in the wild defines), there is usually context missing on what "action" is being triggered by POST/PUT/DELETE endpoints (e.g. many APIs do a delete of a resource via a POST or PUT, and some APIs use DELETE to archive resources)

- baseUrls are often wrong/broken and assumed to be replaced by the API client

- underdocumented AuthZ/AuthN mechanisms (usually only present in the general description comment on the API, and missing on the individual endpoints)

In practice you often have to remedy that by patching the officially distributed OpenAPI specs to make them good enough for a basis of tool calling, making it not-very-plug-and-play.

I think the biggest upside that MCP brings (given all "content"/"functionality") being equal is that using it instead of just plain REST, is that it acts as a badge that says "we had AI usage in mind when building this".

On top of that, MCP also standardizes mechanisms like e.g. elicitation that with traditional REST APIs are completely up to the client to implement.