Either way, I don't do what you suggest. I have self-learning rules and have the models build a well-rounded API engine once, then re-use it with query scripts through skills. Its portable and flexible in many environments.
Not sure whats the norm nowadays, but it used to be MCP descriptions were loaded in from the start.
In any case, to be cheaper the `cli --help` command needs to more noisy than the json description.
Finally, and the really big one: cli can be composed with `grep`, `jq` , etc.
When I use the Atlassian CLI vs their MCP server, I tend to see something like half the token burn with the CLI with more accurate results.
That could be an Atlassian issue but that's the results I'm seeing.
Is using curl considered re-implementing your own api client each time?
If it's just a set of tokens in the current session, well then next session it has to figure out the workflow, and then store it in a way to use in the next session.
Seems like maybe you should take your own advice.
> Seems like maybe you should take your own advice.
Seems like maybe if you have to use your own custom definition of a word in order to support a point you might not have one. lol.
Many apis require several requests to get things done. (one to auth, one or more to fecth resource ids, one or more to modify resources, etc).
That would be a series of curl calls with logic applied to the output of each call to curl. An api client just does those things in a single function call. The steps are the same, but in one case the AI has to figure out each curl call and implement the logic, rather than just call the function.
Irony died in this comment thread.
It makes sense though. It's the same logic that allows you to say promting an AI with "do a simple thing for me" makes you a programmer, and prompting an AI with "what is an api" allows makes you knowledgable about computers.
And When "normal" people talk about api clients? lol This site is wild.