[lang-free]

Overview

Following #57241, apply the existing input_image detail default to list-form function_call_output.output. Currently a tool-returned image without detail fails with ResponseInputImageParam.detail missing, although message images receive the default.

Claims

• Missing detail becomes auto; explicit detail, accompanying text and string outputs are preserved.
• Reuses the existing copy-on-write normalization; caller dictionaries are not mutated.
• Limited to function outputs; custom-tool support is not added.

Validation

Base: c5422f4.
Protocol regression: unchanged implementation with new tests gives 2 failed / 28 passed; candidate gives 30 passed. Cases cover omitted and explicit detail in mixed text/image output, input preservation, string output, multiple images across messages/tool results, and rejection of an illegal detail string.

python -m pytest -p cpu_protocol_tests tests/tool_use/test_responses_request_validations.py -q
pre-commit run --files vllm/entrypoints/openai/responses/protocol.py tests/tool_use/test_responses_request_validations.py

Scoped pre-commit passed. The external CPU-only pytest plugin applies the repository's existing skip_global_cleanup marker, without replacing tested behavior.

Real POST /v1/responses using the complete candidate Python source and previously published official CPU binaries (see environment note below):

Case	Before	After
Text control	200	200
Function-output image, omitted detail	400, missing detail	200, generated output
Function-output image, explicit low/auto/high	200	200
Message image plus two tool-output images, omitted detail	400	200
Same multi-image request, streaming	400	200 with text deltas and response.completed
Tool-output image, explicit null	400	400

Synthetic tiny random Qwen2-VL with synthetic 56x56 images and max_output_tokens=2. The small token budget may end generation with max_output_tokens/incomplete; this is API-path validation, not model accuracy or performance evaluation. OpenAI SDK 3.29.0, Pydantic 2.14.1, torch 2.13.0+cpu. The complete current candidate Python source ran editable with unchanged native CPU extensions from fe4df3d. The current base's CPU wheel was unavailable (404), and the nightly CPU index still pointed to that earlier build. This validates the recorded source/runtime combination, not a matched-revision native rebuild. The before/after phases used the same native binaries.

CPU-only protocol test cleanup marker

The local cpu_protocol_tests.py plugin contains:

import pytest

def pytest_collection_modifyitems(items):
    for item in items:
        if "test_responses_request_validations.py" in str(item.path):
            item.add_marker(pytest.mark.skip_global_cleanup)

The plugin directory was on PYTHONPATH. It opts out of accelerator cleanup on the CPU test environment; it does not replace validation logic. It is not included in the patch.

Details

The helper selects output for function_call_output and content for other items. The existing missing-only normalization and list guard remain unchanged. Custom-tool output is deferred rather than claimed as end-to-end supported.

A matching downstream report exists in maximhq/bifrost#5649; maximhq/bifrost#5659 fixes provider-side conversion, while this candidate fixes vLLM backend normalization.

Related work inspected includes #57241 (message image defaults), #54786 (Harmony text-list handling), and #56285 (other Responses compatibility). This candidate targets function-output image defaults. A focused pre-publication search found no same-scope vLLM fix; related #50782 handles Kimi tool-call IDs rather than image-detail normalization.

AI assistance: Codex assisted with implementation and validation; Claude Opus provided advisory review. The reported local tests were executed by Codex. The human submitter is responsible for understanding and reviewing the contribution end to end.

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Pull Request Checklist

• I used vLLM's /pr-checklist skill.
• AI assistance was used during the creation of this PR.
• Design Fit: existing normalization reused.
• Testing and Validation: the disclosed agent-run protocol and serving checks passed; execution environment and limits are recorded above.
• Code Quality and Style: scoped pre-commit passed.
• Pull Request Contents: main and duplicate checks completed; scope, evidence, limitations and AI assistance disclosed.

[claude]

Claude Code Review

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