[Bugfix] Validate JSON in kimi_k2 tool call arguments

[iOptimizeThings]

Purpose

KimiK2ToolParser.extract_tool_calls() passes regex-extracted arguments directly to FunctionCall.arguments without JSON validation. Malformed JSON (e.g. truncated output from max_tokens) reaches the client as a successful HTTP 200 response, causing downstream json.loads() to crash.

This PR adds json.loads() validation before emitting each tool call. Invalid entries are skipped with a warning log.

Fixes #41739

Test Plan

.venv/bin/python -m pytest tests/tool_parsers/test_kimi_k2_tool_parser.py -v

Test Result

50 passed, 0 failed. Updated test_invalid_json_still_extracted to test_invalid_json_skipped to assert the new behavior (malformed tool calls are skipped, valid ones kept).

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[mergify]

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[gemini-code-assist]

Code Review

This pull request updates the Kimi K2 tool parser to skip tool calls with invalid JSON arguments instead of returning them as-is, with corresponding updates to the test suite. A review comment suggests further refining this validation by ensuring the parsed JSON is specifically a dictionary, which would prevent potential issues with downstream clients that expect a JSON object rather than a primitive value.

[gemini-code-assist]

While validating that the arguments are valid JSON is a good first step, tool call arguments are strictly expected to be a JSON object (dictionary) according to the OpenAI-compatible protocol. If the model generates a valid JSON primitive (like a string, number, or null) instead of an object, downstream clients expecting a dictionary will likely crash when attempting to access parameters. Consider adding a check to ensure the parsed JSON is a dictionary.

                    try:
                        if not isinstance(json.loads(function_args), dict):
                            raise ValueError("Tool call arguments must be a JSON object")
                    except (json.JSONDecodeError, TypeError, ValueError):
                        logger.warning(
                            "Skipping tool call '%s' with invalid JSON: %s",
                            function_name,
                            function_args,
                        )
                        continue

[bbrowning]

I don't think this is really the right thing to do. The general Chat Completions API expectations are that the client is responsible for validating tool call arguments if it needs to before doing anything with them. It cannot just assume the model generates valid JSON all the time.

Even the docs of the original implementation of this API from openai.com says this:

The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.

This becomes even more important when working with agentic workflows and harnesses, where the modern harnesses will validate tool call arguments and if there are any validation errors will send that as a tool response back to the inference server with the validation error so the model can adjust its own output and try again.

If a client cannot handle malformed JSON, the right way to handle that in the Chat Completions or Responses API is to set strict validation of tool schemas in the tool definitions. However, there's a gap here where in most cases we don't wire that up properly in vLLM today. There is another PR I'm reviewing at #43155 that will wire up parts of this specifically for Kimi models.

[iOptimizeThings]

Good point, I completely overlooked how silently dropping the bad JSON would break client-side retry loops. Letting the client catch the raw error is definitely the right call.

I'll close this out in favor of #43155. Thanks for taking the time to explain the design philosophy, super helpful context for the future!

[bbrowning]

You're welcome!