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Exactly. We already have lots of standards for defining APIs (OpenAPI, GraphQL, SOAP if I'm showing my age, etc. etc.) Part of my original "wow this is magic" m

Exactly. We already have lots of standards for defining APIs (OpenAPI, GraphQL, SOAP if I'm showing my age, etc. etc.) Part of my original "wow this is magic" moment with AI came when OpenAI released some of their plugins and showed how you could just point it at an API spec and the LLM could just figure out, on its own, how to use it. So one real beauty of AI is that it is so good at taking "semi structured" data and structuring it. So perhaps I'm missing something, but I don't see how MCP benefits you over existing API documentation formats. It seems like an "old way" of thinking, where we always wanted to define these interoperation contract formats and protocols, but a huge point of AI is you shouldn't really need any more protocols to start with. Again, I don't know all the ins and outs of MCP, so I'm happy to be corrected. It's just that whenever I see examples like in the article, I'm always left wondering what benefit MCP gives you in the first place.

vkazanov 1y ago
Well, one benefit is the precision and focus of the protocol that can be used to train/finetune LLMs. More focused training -> more reliable understanding in LLMs.
- Zigurd 1y ago
  I hear you but what exactly about MCP is more precise or training-friendly than other approaches? I can think of at least one way that it isn't: MCP doesn't provide an API sandbox the way an Apigee or Mulesoft API documentation page could.
  - lgas 1y ago
    There's no reason it couldn't.
- hn_throwaway_99 1y ago
  I understand what you're saying, but I'm still not clear why any of this should be necessary or is a benefit for LLMs. Another commenter mentioned that MCP saves tokens and is more compact. So what? Then just have the LLM do a one-time pass of a more verbose spec to summarize/minify it. Any human brainspace needed to even think about MCP just seems like it goes against the whole raison d'être of AI in that it can synthesize and use disparate information much faster, more efficiently, and cheaper than a human can.
floatrock 1y ago
Don't forget HATEOAS if we're listing prior art of self-discoverable APIs!

vkazanov 1y ago

Well, one benefit is the precision and focus of the protocol that can be used to train/finetune LLMs. More focused training -> more reliable understanding in LLMs.

Zigurd 1y ago
I hear you but what exactly about MCP is more precise or training-friendly than other approaches? I can think of at least one way that it isn't: MCP doesn't provide an API sandbox the way an Apigee or Mulesoft API documentation page could.
- lgas 1y ago
  There's no reason it couldn't.
hn_throwaway_99 1y ago
I understand what you're saying, but I'm still not clear why any of this should be necessary or is a benefit for LLMs. Another commenter mentioned that MCP saves tokens and is more compact. So what? Then just have the LLM do a one-time pass of a more verbose spec to summarize/minify it. Any human brainspace needed to even think about MCP just seems like it goes against the whole raison d'être of AI in that it can synthesize and use disparate information much faster, more efficiently, and cheaper than a human can.

floatrock 1y ago

Don't forget HATEOAS if we're listing prior art of self-discoverable APIs!