[ResponsesAPI] Add GPTOSS MCP tool streaming

tests/entrypoints/openai/test_response_api_mcp_tools.py

@@ -3,9 +3,9 @@
 
 import pytest
 import pytest_asyncio
-from openai import OpenAI
 from openai_harmony import ToolDescription, ToolNamespaceConfig
 
+from openai import OpenAI
 from vllm.entrypoints.tool_server import MCPToolServer
 
 from ...utils import RemoteOpenAIServer
@@ -206,6 +206,65 @@ async def test_mcp_tool_env_flag_disabled(mcp_disabled_client: OpenAI, model_nam
         )
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_mcp_tool_calling_streaming_types(
+    mcp_enabled_client: OpenAI, model_name: str
+):
+    pairs_of_event_types = {
+        "response.completed": "response.created",
+        "response.output_item.done": "response.output_item.added",
+        "response.content_part.done": "response.content_part.added",
+        "response.output_text.done": "response.output_text.delta",
+        "response.reasoning_text.done": "response.reasoning_text.delta",
+        "response.reasoning_part.done": "response.reasoning_part.added",
+        "response.mcp_call_arguments.done": "response.mcp_call_arguments.delta",
+        "response.mcp_call.completed": "response.mcp_call.in_progress",
+    }
+
+    tools = [
+        {
+            "type": "mcp",
+            "server_label": "code_interpreter",
+        }
+    ]
+    input_text = "What is 13 * 24? Use python to calculate the result."
+
+    stream_response = await mcp_enabled_client.responses.create(
+        model=model_name,
+        input=input_text,
+        tools=tools,
+        stream=True,
+        instructions=(
+            "You must use the Python tool to execute code. Never simulate execution."
+        ),
+    )
+
+    stack_of_event_types = []
+    async for event in stream_response:
+        assert "mcp_call" in event.type
+
+        if event.type == "response.created":
+            stack_of_event_types.append(event.type)
+        elif event.type == "response.completed":
+            assert stack_of_event_types[-1] == pairs_of_event_types[event.type]
+            stack_of_event_types.pop()
+        if (
+            event.type.endswith("added")
+            or event.type == "response.mcp_call.in_progress"
+        ):
+            stack_of_event_types.append(event.type)
+        elif event.type.endswith("delta"):
+            if stack_of_event_types[-1] == event.type:
+                continue
+            stack_of_event_types.append(event.type)
+        elif event.type.endswith("done") or event.type == "response.mcp_call.completed":
+            assert stack_of_event_types[-1] == pairs_of_event_types[event.type]
+            stack_of_event_types.pop()
+
+    assert len(stack_of_event_types) == 0
+
+
 def test_get_tool_description():
     """Test MCPToolServer.get_tool_description filtering logic.
 

tests/entrypoints/openai/test_response_api_with_harmony.py

@@ -1,17 +1,18 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import importlib
+import importlib.util
 import json
 import time
 
 import pytest
 import pytest_asyncio
 import requests
-from openai import BadRequestError, NotFoundError, OpenAI
 from openai_harmony import (
     Message,
 )
 
+from openai import BadRequestError, NotFoundError, OpenAI
+
 from ...utils import RemoteOpenAIServer
 
 MODEL_NAME = "openai/gpt-oss-20b"
@@ -43,6 +44,8 @@ def server():
     env_dict = dict(
         VLLM_ENABLE_RESPONSES_API_STORE="1",
         PYTHON_EXECUTION_BACKEND="dangerously_use_uv",
+        VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS="code_interpreter,container,web_search_preview",
+        VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS="1",
     )
 
     with RemoteOpenAIServer(MODEL_NAME, args, env_dict=env_dict) as remote_server:
@@ -850,6 +853,186 @@ async def test_function_calling_with_stream(client: OpenAI, model_name: str):
             assert event.response.output_text is not None
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_mcp_code_interpreter_streaming(client: OpenAI, model_name: str, server):
+    tools = [
+        {
+            "type": "mcp",
+            "server_label": "code_interpreter",
+        }
+    ]
+    input_text = (
+        "Calculate 15 * 32 using python. "
+        "The python interpreter is not stateful and you must print to see the output."
+    )
+
+    stream_response = await client.responses.create(
+        model=model_name,
+        input=input_text,
+        tools=tools,
+        stream=True,
+        temperature=0.0,
+        instructions=(
+            "You must use the Python tool to execute code. Never simulate execution."
+        ),
+    )
+
+    mcp_call_added = False
+    mcp_call_in_progress = False
+    mcp_arguments_delta_seen = False
+    mcp_arguments_done = False
+    mcp_call_completed = False
+    mcp_item_done = False
+
+    code_interpreter_events_seen = False
+
+    async for event in stream_response:
+        if "code_interpreter" in event.type:
+            code_interpreter_events_seen = True
+
+        if event.type == "response.output_item.added":
+            if hasattr(event.item, "type") and event.item.type == "mcp_call":
+                mcp_call_added = True
+                assert event.item.name == "python"
+                assert event.item.server_label == "code_interpreter"
+
+        elif event.type == "response.mcp_call.in_progress":
+            mcp_call_in_progress = True
+
+        elif event.type == "response.mcp_call_arguments.delta":
+            mcp_arguments_delta_seen = True
+            assert event.delta is not None
+
+        elif event.type == "response.mcp_call_arguments.done":
+            mcp_arguments_done = True
+            assert event.name == "python"
+            assert event.arguments is not None
+
+        elif event.type == "response.mcp_call.completed":
+            mcp_call_completed = True
+
+        elif (
+            event.type == "response.output_item.done"
+            and hasattr(event.item, "type")
+            and event.item.type == "mcp_call"
+        ):
+            mcp_item_done = True
+            assert event.item.name == "python"
+            assert event.item.status == "completed"
+
+    assert mcp_call_added, "MCP call was not added"
+    assert mcp_call_in_progress, "MCP call in_progress event not seen"
+    assert mcp_arguments_delta_seen, "MCP arguments delta event not seen"
+    assert mcp_arguments_done, "MCP arguments done event not seen"
+    assert mcp_call_completed, "MCP call completed event not seen"
+    assert mcp_item_done, "MCP item done event not seen"
+
+    assert not code_interpreter_events_seen, (
+        "Should not see code_interpreter events when using MCP type"
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_mcp_tool_multi_turn(client: OpenAI, model_name: str, server):
+    """Test MCP tool calling across multiple turns.
+
+    This test verifies that MCP tools work correctly in multi-turn conversations,
+    maintaining state across turns via the previous_response_id mechanism.
+    """
+    tools = [
+        {
+            "type": "mcp",
+            "server_label": "code_interpreter",
+        }
+    ]
+
+    # First turn - make a calculation
+    response1 = await client.responses.create(
+        model=model_name,
+        input="Calculate 50 * 60 using python and print the result.",
+        tools=tools,
+        temperature=0.0,
+        instructions=(
+            "You must use the Python tool to execute code. Never simulate execution."
+        ),
+        extra_body={"enable_response_messages": True},
+    )
+
+    assert response1 is not None
+    assert response1.status == "completed"
+
+    # Verify MCP call in first response by checking output_messages
+    tool_call_found = False
+    tool_response_found = False
+    for message in response1.output_messages:
+        recipient = message.get("recipient")
+        if recipient and recipient.startswith("python"):
+            tool_call_found = True
+
+        author = message.get("author", {})
+        if (
+            author.get("role") == "tool"
+            and author.get("name")
+            and author.get("name").startswith("python")
+        ):
+            tool_response_found = True
+
+    # Verify MCP tools were actually used
+    assert tool_call_found, "MCP tool call not found in output_messages"
+    assert tool_response_found, "MCP tool response not found in output_messages"
+
+    # Verify input messages: Should have system message with tool, NO developer message
+    developer_messages = [
+        msg for msg in response1.input_messages if msg["author"]["role"] == "developer"
+    ]
+    assert len(developer_messages) == 0, (
+        "No developer message expected for elevated tools"
+    )
+
+    # Second turn - reference previous calculation
+    previous_messages = response1.input_messages + response1.output_messages
+
+    response2 = await client.responses.create(
+        model=model_name,
+        input="Now divide that result by 2.",
+        tools=tools,
+        temperature=0.0,
+        instructions=(
+            "You must use the Python tool to execute code. Never simulate execution."
+        ),
+        extra_body={
+            "previous_input_messages": previous_messages,
+            "enable_response_messages": True,
+        },
+    )
+
+    assert response2 is not None
+    assert response2.status == "completed"
+
+    # Verify input messages are correct: should have two messages -
+    # one to the python recipient on analysis channel and one from tool role
+    mcp_recipient_messages = []
+    tool_role_messages = []
+    for msg in response2.input_messages:
+        if msg["author"]["role"] == "assistant":
+            # Check if this is a message to MCP recipient on analysis channel
+            if msg.get("channel") == "analysis" and msg.get("recipient"):
+                recipient = msg.get("recipient")
+                if recipient.startswith("code_interpreter") or recipient == "python":
+                    mcp_recipient_messages.append(msg)
+        elif msg["author"]["role"] == "tool":
+            tool_role_messages.append(msg)
+
+    assert len(mcp_recipient_messages) > 0, (
+        "Expected message(s) to MCP recipient on analysis channel"
+    )
+    assert len(tool_role_messages) > 0, (
+        "Expected message(s) from tool role after MCP call"
+    )
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 async def test_output_messages_enabled(client: OpenAI, model_name: str, server):