Understanding Tool Calling in LLMs – Step-by-Step with REST and Spring AI

https://muthuishere.medium.com/understanding-tool-function-calling-in-llms-step-by-step-examples-in-rest-and-spring-ai-2149ecd6b18b

by muthuishere • 1 year ago

101 26 1 year ago

muthuishere.medium.com

upghost3• 1 year ago

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.

[1] https://github.com/nexusflowai/NexusRaven-V2

max-privatevoid2• 1 year ago

The actual API call is still going to be JSON. How do you deal with that? Pack your Python function definitions into an array of huge opaque strings? And who would want to write a parser for that?

upghost3• 1 year ago

I'm sure you realize it gets reassembled into "huge opaque strings" when it is fed into the LLM as context. The arbitrary transport of the context as JSON is just a bit of protocol theater.

You don't really have to parse the output, Python already has a parser in the form of the AST library[1].

But I get your drift. Depending on your workflow this could seem like more work.

[1] https://docs.python.org/3/library/ast.html#module-ast

nullorempty• 1 year ago

I don't think Spring is well regarded on HN.

sorokod3• 1 year ago

"Just write this...." adds an annotation

One of the many issues with Spring is that abstractions it provides are extremely leaky [1]. It leaks frequently and when it does, an engineer is faced with the need to comprehend a pile of technology[2] that was supposed to be abstracted away in the first place.

- [1] https://en.wikipedia.org/wiki/Leaky_abstraction

- [2] https://github.com/spring-projects/spring-ai

th0ma5• 1 year ago

I think about this occasionally trying to rationalize it. I see similar patterns in other things like R and Julia where they design something in the environment to seem like a composable tool, and maybe it is but only within two or three specific compositions and then the way the environment is described sure seems to imply some kind of universality but it just doesn't work. Some even seem to keep patching every leak (maybe Spring means Spring a leak? Haha) and there's a sunk cost fallacy thing with an immense documentation page.

xienze• 1 year ago

In what ways are the abstractions leaky? @Tool or @GetMapping make no demands on how to implement “this is a tool” or “this is a GET REST endpoint.” That they’re coupled with Spring (or rather, Spring is the only implementation for the semantics of these annotations) doesn’t constitute a leaky abstraction.

greenchair• 1 year ago

Nothing is well regarded on HN so that's fine.

esafak• 1 year ago

It's a lumbering framework like Django. People opt for lighter and simpler these days.

rapidaneurism• 1 year ago

How do you pass a user token to MCP calls? Do you hand the token to the LLM and expect it to fill an argument?

theblazehen• 1 year ago

Usually via environment variables in the MCP server definition, or a config file

asabla• 1 year ago

I know I'm a bit late. But for MCP servers running over HTTP/custom it should use OAuth 2.0. If it's served via stdout, it should use configuration files and/or environment variables.

ref: https://modelcontextprotocol.io/specification/2025-03-26/bas...

therealpygon• 1 year ago

Easy. The LLM is never making MCP calls. The LLM simply identifies an endpoint it thinks would be useful and provides the required request parameters (like the text to be searched for or processed). As far as an LLM is concerned, MCP calls are handled “client-side” (from its perspective). This is why you configure MCP servers in your client and not on the server. (Yes, some providers allow you to configure MCP servers, but that is just a layer between you and the LLM and not a feature of the LLM itself.

So back to the credentials, that means that the credentials are managed “client-side” and the LLM never needs to see any of that. Think of it like this, say you set up an MCP url (my-mcp.com); the LLM knows nothing of this url, or what MCP server you use. So if instead you called my-mcp.com/<some-long-string>/, the LLM still doesn’t know. Now, instead of a URL parameter, your tool calls the MCP with a header (Bearer: <token>), the LLM still doesn’t know and you’ve accessed an OAUTH endpoint.

_moog2• 1 year ago

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.

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.

youdont• 1 year ago

Have you tried BAML? We use it to manage APIs and clients, as well as prompts and types. It gives great low level control over your prompts and logic, but acts as a nice standardisation later.

_moog2• 1 year ago

That's going to be super useful for some of the high-level prompt-testing work I'm doing. Thanks!

I'm also getting more into the lower-level LLM fine-tuning, training on custom chat templates, etc. which is more of where the diagram was needed.

redhale• 1 year ago

+1 for BAML. I find that the "prompts as typed functions" concept really simplifies the mental model here, making LLM apps easier to reason about.

1dom• 1 year ago

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.

Understanding Tool Calling in LLMs – Step-by-Step with REST and Spring AI

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