LLM function calls don't scale; code orchestration is simpler, more effective
58 comments
·May 21, 2025padjo
_se
No, not most people. But some people are experimenting.
No one has found anything revolutionary yet, but there are some useful applications to be sure.
mehdibl
The issue is not in function calls but HOW MCP got designed here and you are using.
Most MCP are replicating API. Returning blobs of data.
1. This is using a lot of input context in formating as JSON and escaping a Json inside already a JSON. 2. This contain a lot of irrelevant information that you can same on it.
So the issue is the MCP tool. It should instead flaten the data as possible as it's going back again thru JSON Encoding. And if needed remove some fields.
So MCP SAAS here are mainly API gateways.
That brings this noise! And most of ALL they are not optimizing MCP's.
obiefernandez
My team at Shopify just open sourced Roast [1] recently. It lets us embed non-deterministic LLM jobs within orchestrated workflows. Essential when trying to automate work on codebases with millions of lines of code.
TheTaytay
Wow - Roast looks fantastic. You architected and put names and constraints on some things that I've been wrestling with for a while. I really like how you are blending the determinism and non-determinism. (One thing that is not obvious to me after reading the README a couple of times (quickly), is whether/how the LLM can orchestrate multiple tool calls if necessary and make decisions about which tools to call in which order. It seems like it does when you tell it to refactor, but I couldn't tell if this would be suitable for the task of "improve, then run tests. Repeat until done.")
The_Blade
good stuff!
i just broke Claude Code Research Preview, and i've crashed ChatGPT 4.5 Pro Deep Research. and i have the receipts :), so i'm looking for tools that work
drewda
Nice to see Ruby continuing to exist and deliver... even in the age of "AI"
hintymad
I feel that the optimal solution is hybrid, not polarized. That is, we use deterministic approach as much as we can, but leverage LLMs to handle the remaining complex part that is hard to spec out or describe deterministically
jngiam1
Yes - in particular, I think one interesting angle is use the LLM to generate deterministic approaches (code). And then, if the code works, save it for future use and it becomes deterministic moving forward.
nowittyusername
I agree. You want to use as little LLM as possible in your workflows.
codyb
I'm slightly confused as to why you'd use a LLM to sort structured data in the first place?
jngiam1
The goal is to do more complex data processing, like build dashboards, agentically figure out which tickets are stalled, do a quarterly review of things done, etc. Sorting is a tiny task in the bigger ones, but hopefully more easily exemplifies the problem.
kikimora
I don’t understand how this can work. Given probabilistic nature of LLMs the more steps you have more chances something goes off. What is good in the dashboard if you cannot be sure it was not partially hallucinated?
orbital-decay
Probabilistic nature means nothing on its own. LLM that can solve your deterministic task will easily assign 100% to the correct answer (or 99%, the noise floor can be truncated with a sampler). If it doesn't do that and your reply is unstable, it cannot solve it confidently. Which happens to all LLMs on a sufficiently complex task, but it's not related to their probabilistic nature.
Of course that still doesn't mean that you should do that. If you want to maximize model's performance, offload as much distracting stuff as possible to the code.
staunton
> What is good in the dashboard if you cannot be sure it was not partially hallucinated?
A lot of the time the dashboard contents doesn't actually matter anyway, just needs to look pretty...
On a serious note, the systems being built now will eventually be "correct enough most of the time" and that will be good enough (read: cheaper than doing it any other way).
arjunchint
I am kind of confused why can't you just create a new MCP tool that encapsulates parsing and other required steps together in a code block?
This would be more reliable than expecting the LLM to generate working code 100% of the time?
Centigonal
You should for sure do this for common post processing tasks. However, you're usually not going to know all the types of post-processing users will want to do with tool call output at design-time.
bguberfain
I think that there may be another solution for this, that is the LLM write a valid code that calls the MCP's as functions. See it like a Python script, where each MCP is mapped to a function. A simple example:
def process(param1, param2):
my_data = mcp_get_data(param1)
sorted_data = mcp_sort(my_data, by=param2)
return sorted_datajngiam1
Yes! If you want to see how this can work in practice, check out https://lutra.ai ; we've been using a similar pattern there. The challenge is making the code runtime work well for it.
stavros
I would really like to see output-aware LLM inference engines. For example, imagine if the LLM output some tokens that meant "I'm going to do a tool call now", and the inference engine (e.g. llama.cpp) changed the grammar on the fly so the next token could only be valid for the available tools.
Or, if I gave the LLM a list of my users and asked it to filter based on some criteria, the grammar would change to only output user IDs that existed in my list.
I don't know how useful this would be in practice, but at least it would make it impossible for the LLM to hallucinate for these cases.
darkteflon
We’ve been using smolagents, which takes this approach, and are impressed.
Slight tangent, but as a long term user of OpenAI models, I was surprised at how well Claude Sonnet 3.7 through the desktop app handles multi-hop problem solving using tools (over MCP). As long as tool descriptions are good, it’s quite capable of chaining and “lateral thinking” without any customisation of the system or user prompts.
For those of you using Sonnet over API: is this behaviour similar there out of the box? If not, does simply pasting the recently exfiltrated[1] “agentic” prompt into the API system prompt get you (most of the way) there?
3abiton
How does it compare to MCP servers?
darkteflon
Not sure if I correctly understand your question. I was saying that Sonnet 3.7 in the desktop app is good out-of-the-box at orchestrating tools exposed as MCP servers and asking whether that behaviour is also present over the Anthropic API or, if not, whether copy-pasting the exfiltrated system prompt gets you there.
avereveard
That's kind of the entire premise of huggingface smolagent and while it does work really well when it works it also increase the challenges in rolling back failed actions
I guess one could in principle wrap the entire execution block into a distributed transaction, but llm try to make code that is robust, which works against this pattern as it makes hard to understand failure
jngiam1
Agree, the smolagent premise is good; but the hard part is handling execution, errors, etc.
For example, when the code execution fails mid-way, we really want the model to be able to pick up from where it failed (with the states of the variables at the time of failure) and be able to continue from there.
We've found that the LLM is able to generate correct code that picks up gracefully. The hard part now is building the runtime that makes that possible; we've something that works pretty well in many cases now in production at Lutra.
avereveard
I think in principle you can make the entire API exposed to the llm idempotent so that it bicomes irrelevant for the backend wheter the llm replay the whole action or just the failed steps
jngiam1
That'd work well for read-only APIs, but we also want the LLMs to be able to update data, create documents, etc. Feels a bit harder when there are side-effects.
hooverd
Could you implement an actual state machine and have your agent work with that?
avereveard
that's the langraph idea. each langraph node can then be a smolagent
latency tho, would be unbearable for real time.
yahoozoo
In the example request, they want a list of issues in their project but don’t need the ID of each issue. But, what about when you want a list of issues and DO want the ID?
wyett
I had the same question.
norcalkc
> Allowing an execution environment to also access MCPs, tools, and user data requires careful design to where API keys are stored, and how tools are exposed.
If your tools are calling APIs on-behalf of users, it's better to use OAuth flows to enable users of the app to give explicit consent to the APIs/scopes they want the tools to access. That way, tools use scoped tokens to make calls instead of hard to manage, maintain API keys (or even client credentials).
iandanforth
Do you know of any examples which use MCP and oauth cleanly?
Sorry I’ve been out of the industry for the last year or so, is this madness really what people are doing now?