AI can read docs, Swagger, OpenAI and READMEs, so MCP adds nothing here. All you need is an HTTP client with authorization for endpoints.

E.g. in Datomic MCP[^1], I simply tell the model that the tool calls datomic.api/q, and it writes correct Datomic Datalog queries while encoding arguments as EDN strings without any additional READMEs about how EDN works, because AI knows EDN.

And AI knows HTTP requests, it just needs an HTTP client, i.e. we don't need MCP.

So IMO, MCP is an Embrace, Extend (Extinguish?) strategy by Anthropic. The arguments that "foundational model providers don't want to deal with integration at HTTP-level" are uncompelling to me.

All you need is an HTTP client + SSE support + endpoint authz in the client + reasonable timeouts. The API docs will do the rest.

Raw TCP/UDP sockets more dangerous, but people will expose those over MCP anyway.

[^1]: https://github.com/theronic/datomic-mcp/blob/main/src/modex/...