[responsesAPI][5] ResponsesParser with tools for full MCP python loop

[qandrew]

Purpose

This PR is part 2 for the ResponsesParser, which provides the tool parser for responsesParser and the ability to run a MCP python tool.

Not in this PR

• other types of MCP tools like browser: [responsesAPI][7] Browser, Container MCP tools for non harmony models #29989
• logging for tokens
• [responsesAPI][6] input/output messages for ResponsesParser qandrew/vllm#12 supporting raw input / output messages
• there is a shortcoming in this PR where the 2nd turn onwards doesn't properly insert the tokens back in to the engine with the same prefix, due to responses / chat messages mapping to the template. I'll follow up with a PR to fix this.

Test Plan

Added unit tests, and tested the following manually:

Minimax M2

VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS=web_search_preview,container,code_interpreter VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT=1 vllm serve MiniMaxAI/MiniMax-M2   --tensor-parallel-size 4   --tool-call-parser minimax_m2   --reasoning-parser minimax_m2    --enable-auto-tool-choice --trust-remote-code  --tool-server=localhost:8081/container,localhost:8081/browser,localhost:8081/python

curl -X POST "http://localhost:8000/v1/responses"   -H "Content-Type: application/json"   -H "Authorization: Bearer dummy-api-key"   -d '{
        "model": "MiniMaxAI/MiniMax-M2",
        "input": "Multiply 64548*15151 using the python tool.",
        "tools": [
          {
            "type": "mcp",
            "server_label": "code_interpreter",
            "headers": {"test": "test"},
            "server_url": "IGNORED"
          }
        ]
      }'

Kimi K2

VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS=web_search_preview,container,code_interpreter VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT=1 vllm serve moonshotai/Kimi-K2-Thinking   --trust-remote-code   --tensor-parallel-size 8   --enable-auto-tool-choice   --max-num-batched-tokens 32768   --tool-call-parser kimi_k2   --reasoning-parser kimi_k2  --tool-server=localhost:8081/container,localhost:8081/browser,localhost:8081/python

curl -X POST "http://localhost:8000/v1/responses"   -H "Content-Type: application/json"   -H "Authorization: Bearer dummy-api-key"   -d '{
        "model": "moonshotai/Kimi-K2-Thinking",
        "input": "Multiply 64548*15151 using the python tool.",
        "tools": [
          {
            "type": "mcp",
            "server_label": "code_interpreter",
            "headers": {"test": "test"},
            "server_url": "IGNORED"
          }
        ]
      }'

{
    "id": "resp_a42bc867864795cd",
    "created_at": 1764137463,
    "incomplete_details": null,
    "instructions": null,
    "metadata": null,
    "model": "moonshotai/Kimi-K2-Thinking",
    "object": "response",
    "output": [
        {
            "id": "rs_a59c0ff3d139f3ad",
            "summary": [],
            "type": "reasoning",
            "content": [
                {
                    "text": " The user wants me to multiply two numbers: 64548 and 15151. I should use the Python tool to compute this accurately.\n\nLet me set up the calculation. I'll use the arithmetic multiplication operator (*) in Python. ",
                    "type": "reasoning_text"
                }
            ],
            "encrypted_content": null,
            "status": null
        },
        {
            "id": "lol",
            "arguments": "{\"code\": \"result = 64548 * 15151\\nresult\", \"restart\": false}",
            "name": "code_interpreter",
            "server_label": "code_interpreter",
            "type": "mcp_call",
            "approval_request_id": null,
            "error": null,
            "output": "977966748\n",
            "status": "completed"
        },
        {
            "id": "rs_818e3eeeb7e9efa7",
            "summary": [],
            "type": "reasoning",
            "content": [
                {
                    "text": " The result of multiplying 64548 by 15151 is **977,966,748**. ",
                    "type": "reasoning_text"
                }
            ],
            "encrypted_content": null,
            "status": null
        },
        {
            "id": "msg_bf62d1a50301381c",
            "content": [
                {
                    "annotations": [],
                    "text": " The result of multiplying 64548 by 15151 is **977,966,748**.",
                    "type": "output_text",
                    "logprobs": null
                }
            ],
            "role": "assistant",
            "status": "completed",
            "type": "message"
        }
    ],
    "parallel_tool_calls": true,
    "temperature": 1.0,
    "tool_choice": "auto",
    "tools": [
        {
            "server_label": "code_interpreter",
            "type": "mcp",
            "allowed_tools": null,
            "authorization": null,
            "connector_id": null,
            "headers": {
                "test": "test"
            },
            "require_approval": null,
            "server_description": null,
            "server_url": "IGNORED"
        }
    ],
    "top_p": 1.0,
    "background": false,
    "max_output_tokens": 261990,
    "max_tool_calls": null,
    "previous_response_id": null,
    "prompt": null,
    "reasoning": null,
    "service_tier": "auto",
    "status": "completed",
    "text": null,
    "top_logprobs": null,
    "truncation": "disabled",
    "usage": {
        "input_tokens": 154,
        "input_tokens_details": {
            "cached_tokens": 64,
            "input_tokens_per_turn": [],
            "cached_tokens_per_turn": []
        },
        "output_tokens": 121,
        "output_tokens_details": {
            "reasoning_tokens": 0,
            "tool_output_tokens": 0,
            "output_tokens_per_turn": [],
            "tool_output_tokens_per_turn": []
        },
        "total_tokens": 275
    },
    "user": null,
    "input_messages": null,
    "output_messages": null
}

[mergify]

Documentation preview: https://vllm--29798.org.readthedocs.build/en/29798/

[qandrew]

cc @chaunceyjiang @yeqcharlotte ready for review (:

[chatgpt-codex-connector]

💡 Codex Review

vllm/vllm/entrypoints/openai/serving_responses.py

Lines 312 to 314 in a57e4d8

	import fbvscode
	fbvscode.set_trace()

Drop debugger import that halts responses API

create_responses now immediately imports fbvscode and calls set_trace() before any validation. fbvscode is not a declared dependency, so every responses request will either raise ModuleNotFoundError or break into a debugger, preventing the endpoint from serving responses at all. This is a hard blocker for the responses API. (vllm/entrypoints/openai/serving_responses.py:312-314)

---

vllm/vllm/entrypoints/context.py

Lines 281 to 285 in a57e4d8

	message = ResponseFunctionToolCallOutputItem(
	id=f"fco_{random_uuid()}",
	type="function_call_output",
	call_id=f"call_{random_uuid()}",
	output=result_str,

Preserve tool call id when emitting python tool output

call_python_tool creates the ResponseFunctionToolCallOutputItem with a new random call_id instead of reusing the call_id from the preceding function_call. When the next turn is rendered, construct_input_messages uses the output item’s call_id as the tool_call_id (see vllm/entrypoints/responses_utils.py:141-146), so the tool result is associated with an id that does not match the assistant’s tool call. This breaks multi‑turn python/mcp tool conversations under ParsableContext because the model cannot link tool output to the original call. (vllm/entrypoints/context.py:281-285)

ℹ️ About Codex in GitHub

Codex has been enabled to automatically review pull requests in this repo. Reviews are triggered when you

• Open a pull request for review
• Mark a draft as ready
• Comment "@codex review".

If Codex has suggestions, it will comment; otherwise it will react with 👍.

When you sign up for Codex through ChatGPT, Codex can also answer questions or update the PR, like "@codex address that feedback".

[heheda12345]

CC @yeqcharlotte

[chaunceyjiang]

type?

[chaunceyjiang]

Why not import it at the top?

[qandrew]

it was similar in for HarmonyContext. I think if we move to the top we get a circular import

[chaunceyjiang]

Thanks~

[yeqcharlotte]

for all the oai entrypoint logic we add can we introduce some unit tests? also how adapter is it for anthropic apis?

[yeqcharlotte]

oh why was this commented before? did it have issues with ci?

[qandrew]

i left it there in a previous PR because we didn't have tool calling yet, so it wasn't necessary yet. There weren't any CI issues

[yeqcharlotte]

this format is quite bad lol. let's directly check the condition. also should we hardcode "code_interpreter", "python" here? i remember @alecsolder made the changes to centralize all tools to go through mcp tool type.

if xxxx:
    return True
return False

[qandrew]

i was thinking to clean up the code in #29989, which will include browser & container tool if that's okay? This PR is just to complete the ability to call only the python tool lol

[qandrew]

for all the oai entrypoint logic we add can we introduce some unit tests? also how adapter is it for anthropic apis?

I added some unit tests in this PR in tests/entrypoints/openai/test_response_api_parsable_context.py :)
Right now, going from ResponsesAPI <-> ChatCompletions is pretty easy with https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/responses_utils.py#L44. I think it should be pretty adaptable; one way to do it is to have a ResponsesAPI <-> MessagesAPI converter, or we could write a MessagesParser similar to what we have in ResponsesParser.