A few things: in this case, you have to provide the tool list in your prompt for the AI to know it exists. But you probably want the AI agent to be able to act and choose tools without you micromanaging and reminding it in every prompt, so then you'd need a tool list... and then you're back to providing the tool list automatically ala MCP again.

MCP can provide validation & verification of the request before making the API call. Giving the model a /tool/forecast URL doesn't prevent the model from deciding to instead explore what other tools might be available on the remote server instead, like deciding to try running /tool/imagegenerator or /tool/globalthermonuclearwar. MCP can gatekeep what the AI does, check that parameters are valid, etc.

Also, MCP can be used to do local computation, work with local files etc, things that web access wouldn't give you. CLI will work for some of those use cases too, but there is a maximum command line length limit, so you might struggle to write more than 8kB to a file when using the command line, for example. It can be easier to get MCP to work with binary files as well.

I tend to think of local MCP servers like DLLs, except the function calls are over stdio and use tons of wasteful JSON instead of being a direct C-function call. But thinking of where you might use a DLL and where you might call out to a CLI can be a useful way of thinking about the difference.