[responsesAPI][6] Fix multi turn MCP tokenization (#30230)

Signed-off-by: Andrew Xia <axia@fb.com>
Co-authored-by: Andrew Xia <axia@fb.com>

tests/entrypoints/test_responses_utils.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import pytest
+from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall
 from openai.types.responses.response_function_tool_call_output_item import (
     ResponseFunctionToolCallOutputItem,
 )
@@ -14,7 +15,8 @@ from openai.types.responses.response_reasoning_item import (
 )
 
 from vllm.entrypoints.responses_utils import (
-    construct_chat_message_with_tool_call,
+    _construct_single_message_from_response_item,
+    construct_chat_messages_with_tool_call,
     convert_tool_responses_to_completions_format,
 )
 
@@ -42,7 +44,43 @@ class TestResponsesUtils:
 
         assert result == {"type": "function", "function": input_tool}
 
-    def test_construct_chat_message_with_tool_call(self):
+    def test_construct_chat_messages_with_tool_call(self):
+        """Test construction of chat messages with tool calls."""
+        reasoning_item = ResponseReasoningItem(
+            id="lol",
+            summary=[],
+            type="reasoning",
+            content=[
+                Content(
+                    text="Leroy Jenkins",
+                    type="reasoning_text",
+                )
+            ],
+            encrypted_content=None,
+            status=None,
+        )
+        mcp_tool_item = ResponseFunctionToolCall(
+            id="mcp_123",
+            call_id="call_123",
+            type="function_call",
+            status="completed",
+            name="python",
+            arguments='{"code": "123+456"}',
+        )
+        input_items = [reasoning_item, mcp_tool_item]
+        messages = construct_chat_messages_with_tool_call(input_items)
+
+        assert len(messages) == 1
+        message = messages[0]
+        assert message["role"] == "assistant"
+        assert message["reasoning"] == "Leroy Jenkins"
+        assert message["tool_calls"][0]["id"] == "call_123"
+        assert message["tool_calls"][0]["function"]["name"] == "python"
+        assert (
+            message["tool_calls"][0]["function"]["arguments"] == '{"code": "123+456"}'
+        )
+
+    def test_construct_single_message_from_response_item(self):
         item = ResponseReasoningItem(
             id="lol",
             summary=[],
@@ -56,7 +94,7 @@ class TestResponsesUtils:
             encrypted_content=None,
             status=None,
         )
-        formatted_item = construct_chat_message_with_tool_call(item)
+        formatted_item = _construct_single_message_from_response_item(item)
         assert formatted_item["role"] == "assistant"
         assert formatted_item["reasoning"] == "Leroy Jenkins"
 
@@ -74,7 +112,7 @@ class TestResponsesUtils:
             status=None,
         )
 
-        formatted_item = construct_chat_message_with_tool_call(item)
+        formatted_item = _construct_single_message_from_response_item(item)
         assert formatted_item["role"] == "assistant"
         assert (
             formatted_item["reasoning"]
@@ -88,7 +126,7 @@ class TestResponsesUtils:
             output="1234",
             status="completed",
         )
-        formatted_item = construct_chat_message_with_tool_call(tool_call_output)
+        formatted_item = _construct_single_message_from_response_item(tool_call_output)
         assert formatted_item["role"] == "tool"
         assert formatted_item["content"] == "1234"
         assert formatted_item["tool_call_id"] == "temp"
@@ -102,7 +140,7 @@ class TestResponsesUtils:
             status=None,
         )
         with pytest.raises(ValueError):
-            construct_chat_message_with_tool_call(item)
+            _construct_single_message_from_response_item(item)
 
         output_item = ResponseOutputMessage(
             id="msg_bf585bbbe3d500e0",
@@ -119,6 +157,6 @@ class TestResponsesUtils:
             type="message",
         )
 
-        formatted_item = construct_chat_message_with_tool_call(output_item)
+        formatted_item = _construct_single_message_from_response_item(output_item)
         assert formatted_item["role"] == "assistant"
         assert formatted_item["content"] == "dongyi"

vllm/entrypoints/constants.py

@@ -8,3 +8,5 @@ Shared constants for vLLM entrypoints.
 # These constants help mitigate header abuse attacks
 H11_MAX_INCOMPLETE_EVENT_SIZE_DEFAULT = 4194304  # 4 MB
 H11_MAX_HEADER_COUNT_DEFAULT = 256
+
+MCP_PREFIX = "mcp_"

vllm/entrypoints/context.py

@@ -19,6 +19,7 @@ from vllm import envs
 from vllm.entrypoints.chat_utils import (
     ChatTemplateContentFormatOption,
 )
+from vllm.entrypoints.constants import MCP_PREFIX
 from vllm.entrypoints.openai.parser.harmony_utils import (
     get_encoding,
     get_streamable_parser_for_assistant,
@@ -303,7 +304,7 @@ class ParsableContext(ConversationContext):
         result_str = result.content[0].text
 
         message = ResponseFunctionToolCallOutputItem(
-            id=f"fco_{random_uuid()}",
+            id=f"mcpo_{random_uuid()}",
             type="function_call_output",
             call_id=f"call_{random_uuid()}",
             output=result_str,
@@ -385,6 +386,9 @@ class ParsableContext(ConversationContext):
         if not self.parser.response_messages:
             return []
         last_msg = self.parser.response_messages[-1]
+        # change this to a mcp_ function call
+        last_msg.id = f"{MCP_PREFIX}{random_uuid()}"
+        self.parser.response_messages[-1] = last_msg
         if last_msg.name == "code_interpreter":
             return await self.call_python_tool(self._tool_sessions["python"], last_msg)
         elif last_msg.name == "web_search_preview":

vllm/entrypoints/openai/serving_engine.py

@@ -1339,6 +1339,7 @@ class OpenAIServing:
                 )
                 engine_prompt = engine_prompts[0]
                 request_prompt = request_prompts[0]
+                prompt_text, _, _ = self._get_prompt_components(request_prompt)
 
             # Update the sampling params.
             sampling_params.max_tokens = self.max_model_len - len(

vllm/entrypoints/responses_utils.py

@@ -22,6 +22,7 @@ from openai.types.responses.response_reasoning_item import ResponseReasoningItem
 from openai.types.responses.tool import Tool
 
 from vllm import envs
+from vllm.entrypoints.constants import MCP_PREFIX
 from vllm.entrypoints.openai.protocol import (
     ChatCompletionMessageParam,
     ResponseInputOutputItem,
@@ -44,13 +45,13 @@ def make_response_output_items_from_parsable_context(
                 )
             if isinstance(output_messages[-1], ResponseFunctionToolCall):
                 mcp_message = McpCall(
-                    id=f"mcp_{random_uuid()}",
+                    id=f"{MCP_PREFIX}{random_uuid()}",
                     arguments=output_messages[-1].arguments,
                     name=output_messages[-1].name,
                     server_label=output_messages[
                         -1
                     ].name,  # TODO: store the server label
-                    type="mcp_call",
+                    type=f"{MCP_PREFIX}call",
                     status="completed",
                     output=message.output,
                     # TODO: support error output
@@ -98,12 +99,63 @@ def construct_input_messages(
     if isinstance(request_input, str):
         messages.append({"role": "user", "content": request_input})
     else:
-        for item in request_input:
+        input_messages = construct_chat_messages_with_tool_call(request_input)
-            messages.append(construct_chat_message_with_tool_call(item))
+        messages.extend(input_messages)
     return messages
 
 
-def construct_chat_message_with_tool_call(
+def _maybe_combine_reasoning_and_tool_call(
+    item: ResponseInputOutputItem, messages: list[ChatCompletionMessageParam]
+) -> ChatCompletionMessageParam | None:
+    """Many models treat MCP calls and reasoning as a single message.
+    This function checks if the last message is a reasoning message and
+    the current message is a tool call"""
+    if not (
+        isinstance(item, ResponseFunctionToolCall) and item.id.startswith(MCP_PREFIX)
+    ):
+        return None
+    if len(messages) == 0:
+        return None
+    last_message = messages[-1]
+    if not (
+        last_message.get("role") == "assistant"
+        and last_message.get("reasoning") is not None
+    ):
+        return None
+
+    last_message["tool_calls"] = [
+        ChatCompletionMessageToolCallParam(
+            id=item.call_id,
+            function=FunctionCallTool(
+                name=item.name,
+                arguments=item.arguments,
+            ),
+            type="function",
+        )
+    ]
+    return last_message
+
+
+def construct_chat_messages_with_tool_call(
+    input_messages: list[ResponseInputOutputItem],
+) -> list[ChatCompletionMessageParam]:
+    """This function wraps _construct_single_message_from_response_item
+    Because some chatMessages come from multiple response items
+    for example a reasoning item and a MCP tool call are two response items
+    but are one chat message
+    """
+    messages: list[ChatCompletionMessageParam] = []
+    for item in input_messages:
+        maybe_combined_message = _maybe_combine_reasoning_and_tool_call(item, messages)
+        if maybe_combined_message is not None:
+            messages[-1] = maybe_combined_message
+        else:
+            messages.append(_construct_single_message_from_response_item(item))
+
+    return messages
+
+
+def _construct_single_message_from_response_item(
     item: ResponseInputOutputItem,
 ) -> ChatCompletionMessageParam:
     if isinstance(item, ResponseFunctionToolCall):