[cle]

I just tried this in Claude Code. I made an MCP server whose tool output is declared as an integer but it returns a string at runtime.

Claude Code validated the response against the schema and did not pass the response to the LLM.

     test - test_tool (MCP)(input: "foo")
      ⎿  Error: Output validation error: 'bar' is not of type 'integer'

[whoknowsidont]

How many times does this need to be repeated.

It works in this instance. On this run. It is not guaranteed to work next time. There is a error percentage here that makes it _INEVITABLE_ that eventually, with enough executions, the validation will pass when it should fail.

It will choose not to pass this to the validator, at some point in the future. It will create its own validator, at some point in the future. It will simply pretend like it did any of the above, at some point in the future.

This might be fine for your B2B use case. It is not fine for underlying infrastructure for a financial firm or communications.

[cle]

Every time the LLM uses this tool, the response schema is validated--deterministically. The LLM will never see a non-integer value as output from the tool.

[whoknowsidont]

Can you please diagram out, using little text arrows ("->"), what you think is happening so I can just fill in the gap for you?

[cle]

I write these as part of my job, I know how they work. I'm not going to spend more time explaining to you (and demonstrating!) what is in the spec. Read the spec and let the authors know that they don't understand what they wrote. I've run out of energy in this conversation.

[whoknowsidont]

I gave you the chance to be explicit about your mental model of these systems and you run away with very unoriginal grandstand.

[masafej536]

llm tool call -> mcp client validates the schema -> mcp client calls the tool -> mcp server validates the schema -> mcp server responds with the result -> mcp client passes the tool result into llm

[redandblack]

not a developer.

what happens if this schema validation fails here - what will the mcp server respond with and what will the llm do next (in a deterministic sense)?

llm tool call -> mcp client validates the schema -> mcp client calls the tool -> mcp server validates the schema

[masafej536]

They often do fail, at the client level you can just feed the schema validation error message back into the LLM and it corrects itself most of the time. If not the LLM throws itself into a loop until its caller times it out and it sends an error message back to the user.

At the server level it's just a good old JSON API at this point, and the server would send the usual error message it would send out to anyone.

[ohdeargodno]

This time.

Can you guarantee it will validate it every time ? Can you guarantee the way MCPs/tool calling are implemented (which is already an incredible joke that only python brained developers would inflict upon the world) will always go through the validation layer, are you even sure of what part of Claude handles this validation ? Sure, it didn't cast an int into a Toyota Yaris. Will it cast "70Y074" into one ? Maybe a 2022 one. What if there are embedded parsing rules into a string, will it respect it every time ? What if you use it outside of Claude Code, but just ask nicely through the API, can you guarantee this validation still works ? Or that they won't break it next week ?

The whole point of it is, whichever LLM you're using is already too dumb to not trip when lacing its own shoes. Why you'd trust it to reliably and properly parse input badly described by a terrible format is beyond me.

[dragonwriter]

> Can you guarantee it will validate it every time ?

Yes, to the extent you can guarantee the behavior of third party software, you can (which you can't really guarantee no matter what spec the software supposedly implements, so the gaps aren't an MCP issue), because “the app enforces schema compliance before handing the results to the LLM” is deterministic behavior in the traditional app that provides the toolchain that provides the interface between tools (and the user) and the LLM, not non-deterministic behavior driven by the LLM. Hence, “before handing the results to the LLM”.

> The whole point of it is, whichever LLM you're using is already too dumb to not trip when lacing its own shoes. Why you'd trust it to reliably and properly parse input badly described by a terrible format is beyond me.

The toolchain is parsing, validating, and mapping the data into the format preferred by the chosen models promot template, the LLM has nothing to do with doing that, because that by definition has to happen before it can see the data.

You aren't trusting the LLM.

[whoknowsidont]

>The toolchain is parsing, validating, and mapping the data into the format preferred by the chosen models promot template, the LLM has nothing to do with doing that

The LLM has everything to do with that. The LLM is literally choosing to do that. I don't know why this point keeps getting missed or side-stepped.

It WILL, at some point in the future and given enough executions, as a matter of statistical certainty, simply not do that above, or pretend to do the above, or do something totally different at some point in the future.

[dragonwriter]

> The LLM has everything to do with that. The LLM is literally choosing to do that.

No, the LLM doesn't control on a case-by-caae basis what the toolchain does between the LLM putting a tool call request in an output message and the toolchain calling the LLM afterwards.

If the toolchain is programmed to always validate tool responses against the JSON schema provided by MCP server before mapping into the LLM prompt template and calling the LLM again to handle the response, that is going to happen 100% of the time. The LLM doesn't choose it. It CAN'T because the only way it even knows that the data has come back from the tool call is that the toolchain has already done whatever it is programmed to do, ending with mapping the response into a prompt and calling the LLM again.

Even before MCPs or even models specifically trained and with vendor-provided templates for tool calling (but after the ReAct architecture was described), it was like a weekend project to implement a basic framework supporting tooling calling around a local or remote LLM. I don't think you need to do that to understand how silly the claim that the LLM controls what the toolchain does with each response and might make it not validate it is, but certainly doing it will give you a visceral understanding of how silly it is.

[whoknowsidont]

I think you are, for whatever reason, missing a fact of causality here and I'm not sure I can fix that over text. I mean that in the most respectful way possible.

[gnat]

Are you two talking at cross-purposes because you don't have a shared understanding of control and data flow?

The pieces here are:

* Claude Code, a Node (Javascript) application that talks to MCP server(s) and the Claude API

* The MCP server, which exposes some tools through stdin or HTTP

* The Claude API, which is more structured than "text in, text out".

* The Claude LLM behind the API, which generates a response to a given prompt

Claude Code is a Node application. CC is configured in JSON with a list of MCP servers. When CC starts up, CC"s Javascript initialises each server and as part of that gets a list of callable functions.

When CC calls the LLM API with a user's request, it's not just "here is the user's words, do it". There are multiple slots in the request object, one of which is a "tools" block, a list of the tools that can be called. Inside the API, I imagine this is packaged into a prefix context string like "you have access to the following tools: tool(args) ...". The LLM API probably has a bunch of prompts it runs through (figure out what type of request the user has made, maybe using different prompts to make different types of plan, etc.) and somewhere along the way the LLM might respond with a request to call a tool.

The LLM API call then returns the tool call request to CC, in a structured "tool_use" block separate from the freetext "hey good news, you asked a question and got this response". The structured block means "the LLM wants to call this tool."

CC's JS then calls the server with the tool request and gets the response. It validates the response (e.g., JSON schemas) and then calls the LLM API again bundling up the success/failure of the tool call into a structured "tool_result" block. If it validated and was successful, the LLM gets to see the MCP server's response. If it failed to validate, the LLM gets to see that it failed and what the error message was (so the LLM can try again in a different way).

The idea is that if a tool call is supposed to return a CarMakeModel string ("Toyota Tercel") and instead returns an int (42), JSON Schemas can catch this. The client validates the server's response against the schema, and calls the LLM API with

  {
    "type": "tool_result",
    "tool_use_id": "abc123",
    "is_error": true,
    "content": [
      {
        "type": "text",
        "text": "Expected string, got integer."
      }
    ]
  }

So the LLM isn't choosing to call the validator, it's the deterministic Javascript that is Claude Code that chooses to call the validator.

There are plenty of ways for this to go wrong: the client (Claude Code) has to validate; int vs string isn't the same as "is a valid timestamp/CarMakeModel/etc"; if you helpfully put the thing that failed into the error message ("Expect string, got integer (42)") then the LLM gets 42 and might choose to interpret that as a CarMakeModel if it's having a particularly bad day; the LLM might say "well, that didn't work, but let's assume the answer was Toyota Tercel, a common car make and model", ... We're reaching here, yet these are possible.

But the basic flow has validation done in deterministic code and hiding the MCP server's invalid responses from the LLM. The LLM can't choose not to validate. You seemed to be saying that the LLM could choose not to validate, and your interlocutor was saying that was not the case.

I hope this helps!

[whartung]

I don’t know how this works, just to start off.

How does the AI bypass the MCP layer to make the request? The assumption is (as I understand it) the AI says “I want to make MCP request XYZ with data ABC” and it sends that off to the MCP interface which does the heavy lifting.

If the MCP interface is doing the schema checks, and tossing errors as appropriate, how is the AI routing around this interface to bypass the schema enforcement?

[whoknowsidont]

>How does the AI bypass the MCP layer to make the request

It doesn't. I don't know why the other commenters are pretending this step does not happen.

There is a prompt that basically tells the LLM to use the generated manifest/configuration files. The LLM still has to not hallucinate in order to properly call the tools with JRPC and properly follow MCP protocol. It then also has to make sense of the structured prompts that define the tools in the MCP manifest/configuration file.

It's system prompts all the way down. Here's a good read of some the underlying/supporting concepts: https://huggingface.co/docs/hugs/en/guides/function-calling

Why this fact is seemingly being lost in this thread, I have no idea, but I don't have anything nice to say about it so I won't :). Other than we're all clearly quite screwed, of course.

MCP is to make things standard for humans, with expected formats. The LLM's really couldn't give a shit and don't have anything super special about how the interact with MCP configuration files or the protocol (other than some additional fine-tuning, again, to make it less likely to get the wrong output).

[dragonwriter]

> There is a prompt that basically tells the LLM to use the generated manifest/configuration files.

No, there isn't. The model doesn't see any difference between MCP-supplied tools, tools built in to the toolchain, and tools supplied by any other method. The prompt simply provides tool names, arguments, and response types to the model. The toolchain, a conventional deterministic program, reads the model response, finds things that meet the models defined format for tool calls, parses out the call names and arguments, looks up in its own internal list of tools to find matching names and see if they are internal, MCP supplied, or other tools, and routes the calls appropriately, gathers responses, does any validation it is designed to do, then mals the validated results into where the model's prompt template specifies tool results should go, and calls the model again with an new message appended to the previous conversation context containing the tool results.

[12345hn6789]

Do you have any technical diagrams or specs that describe this flow? I've been reading the Lang chain[0] and mcp docs[0] and cannot find this behavior you're proposing anywhere.

[0]- https://langchain-ai.github.io/langgraph/agents/mcp/

[1]- https://docs.anthropic.com/en/docs/mcp

[cle]

What you described is essentially how it works. The LLM has no control over how the inputs & outputs are validated, nor in how the result is fed back into it.

The MCP interface (Claude Code in this case) is doing the schema checks. Claude Code will refuse to provide the result to the LLM if it does not pass the schema check, and the LLM has no control over that.

[whoknowsidont]

>The LLM has no control over how the inputs & outputs are validated

Which is completely fucking irrelevant to what everyone else is discussing.

[cle]

This is deterministic, it is validating the response using a JSON Schema validator and refusing to pass it to an LLM inference.

I can't gaurantee that behavior will remain the same more than any other software. But all this happens before the LLM is even involved.

> The whole point of it is, whichever LLM you're using is already too dumb to not trip when lacing its own shoes. Why you'd trust it to reliably and properly parse input badly described by a terrible format is beyond me.

You are describing why MCP supports JSON Schema. It requires parsing & validating the input using deterministic software, not LLMs.

[whoknowsidont]

>This is deterministic, it is validating the response using a JSON Schema validator and refusing to pass it to an LLM inference.

No. It is not. You are still misunderstanding how this works. It is "choosing" to pass this to a validator or some other tool, _for now_. As a matter of pure statistics, it will simply not do this at some point in the future on some run.

It is inevitable.

[cle]

I'd encourage you to read the MCP specification: https://modelcontextprotocol.io/specification/2025-06-18/ser...

Or write a simple MCP server and a client that uses it. FastMCP is easy: https://gofastmcp.com/getting-started/quickstart

You are quite wrong. The LLM "chooses" to use a tool, but the input (provided by the LLM) is validated with JSON Schema by the server, and the output is validated by the client (Claude Code). The output is not provided back to the LLM if it does not comply with the JSON Schema, instead an error is surfaced.

[justinclift]

> The LLM "chooses" to use a tool

I think the others are trying to point out that statistically speaking, in at least one run the LLM might do something other than choose to use the correct tool. i.e 1 out of (say) 1 million runs it might do something else

[dragonwriter]

No, the discussion is about whether validation is certain to happen when the LLM makes something where the frontend recognizes aa a tool request and calls a tool on behalf of the LLM, not whether the LLM can choose not to make a tool call at all.

The question is whether havign observed Claude Code validating a tool response before handing the response back to the LLM, you can count on that validation on future calls, not whether you can count on the LLM calling a tool in a similar situation.

[whoknowsidont]

Why do you think anything you said contradicts what I'm saying? I promise you I'm probably far more experienced in this field than you are.

>The LLM "chooses" to use a tool

Take a minute to just repeat this a few times.

[fauigerzigerk]

MCP requires that servers providing tools must deterministically validate tool inputs and outputs against the schema.

LLMs cannot decide to skip this validation. They can only decide not to call the tool.

So is your criticism that MCP doesn't specify if and when tools are called? If so then you are essentially asking for a massive expansion of MCP's scope to turn it into an orchestration or workflow platform.

[dragonwriter]

The LLM chooses to call a tool, it doesn't choose how the frontend handles anything about that call between the LLM making a tool request and the frontend, after having done its processing of the response (including any validation), mapping the result into a new prompt and calling the LLM with it.

[dragonwriter]

> . It is "choosing" to pass this to a validator or some other tool, _for now_.

No, its not. The validation happens at the frontend before the LLM sees the response. There is no way for the LLM to choose anything about what happens.

The cool thing about having coded a basic ReAct pattern implementation (before MCP, or even models trained on any specific prompt format for tool calls, was a thing, but none of that impacts the basic pattern) is that it gives a pretty visceral understanding of what is going on here, and all that's changed since is per model standardization of prompt and response patterns on the frontend<->LLM side and, with MCP, of the protocol for interacting on the frontend<->tool side.

[astrange]

Claude Code isn't a pure LLM, it's a regular software program that calls out to an LLM with an API. The LLM is not making any decisions about validation.

[thwarted]

As an example.

"1979010112345" is a unix timestamp that looks like it might be Jan 1 1979 datetime formatted as an integer, but is really Sep 17 2032 05:01:52.