I think it's interesting and odd that tool calling took the form of this gnarly json blob. I much prefer the NexusRaven[1] style where you provide python function stubs with docstrings and get back python function invocations with the arguments populated. Of course I don't really understand why MCP is popular over REST or CLI, either.
I recently started diving into LLMs a few weeks ago, and one thing that immediately caught me off guard was how little standardization there is across all the various pieces you would use to build a chat stack.
Want to swap out your client for a different one? Good luck - it probably expects a completely different schema. Trying a new model? Hope you're ready to deal with a different chat template. It felt like every layer had its own way of doing things, which made understanding the flow pretty frustrating for a noobie.
So I sketched out a diagram that maps out what (rough) schema is being used at each step of the process - from the initial request all the way through Ollama and an MCP server with OpenAI-compatible endpoints showing what transformations occur where.
I found this really helpful. I've read a few different bits around this area, and being able to quickly click and scroll around this has confirmed my understanding of it now - thanks!
I thought it funny to think how this is all to give the impression to the user that the AI, for example, _knows_ the weather. The AI doesn't: it's just getting it from a weather API and wrapping some text around it.
Now, imagine being given a requirement 5 years ago like: "When the user asks, we need to be able to show them the weather from this API, and wrap some text around it". Imagine something like your diagram came back as the proposed the solution:| Not at all a criticism of any of your stuff, but it blows my mind how tech develops.
5 comments
[ 6.0 ms ] story [ 25.4 ms ] thread[1]: https://github.com/nexusflowai/NexusRaven-V2
Want to swap out your client for a different one? Good luck - it probably expects a completely different schema. Trying a new model? Hope you're ready to deal with a different chat template. It felt like every layer had its own way of doing things, which made understanding the flow pretty frustrating for a noobie.
So I sketched out a diagram that maps out what (rough) schema is being used at each step of the process - from the initial request all the way through Ollama and an MCP server with OpenAI-compatible endpoints showing what transformations occur where.
Figured I'd share it as it may help someone else.
https://moog.sh/posts/openai_ollama_mcp_flow.html
Somewhat ironically, Claude built the JS hooks for my SVG with about five minutes of prompting.
I thought it funny to think how this is all to give the impression to the user that the AI, for example, _knows_ the weather. The AI doesn't: it's just getting it from a weather API and wrapping some text around it.
Now, imagine being given a requirement 5 years ago like: "When the user asks, we need to be able to show them the weather from this API, and wrap some text around it". Imagine something like your diagram came back as the proposed the solution:| Not at all a criticism of any of your stuff, but it blows my mind how tech develops.