diff --git a/llama_stack/providers/inline/agents/meta_reference/openai_responses.py b/llama_stack/providers/inline/agents/meta_reference/openai_responses.py index 6aca4d68e..d454683ec 100644 --- a/llama_stack/providers/inline/agents/meta_reference/openai_responses.py +++ b/llama_stack/providers/inline/agents/meta_reference/openai_responses.py @@ -55,6 +55,7 @@ from llama_stack.apis.agents.openai_responses import ( OpenAIResponseOutputMessageFileSearchToolCall, OpenAIResponseOutputMessageFileSearchToolCallResults, OpenAIResponseOutputMessageFunctionToolCall, + OpenAIResponseOutputMessageMCPCall, OpenAIResponseOutputMessageMCPListTools, OpenAIResponseOutputMessageWebSearchToolCall, OpenAIResponseText, @@ -163,6 +164,19 @@ async def _convert_response_input_to_chat_messages( ), ) messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call])) + elif isinstance(input_item, OpenAIResponseOutputMessageMCPCall): + tool_call = OpenAIChatCompletionToolCall( + index=0, + id=input_item.id, + function=OpenAIChatCompletionToolCallFunction( + name=input_item.name, + arguments=input_item.arguments, + ), + ) + messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call])) + elif isinstance(input_item, OpenAIResponseOutputMessageMCPListTools): + # the tool list will be handled separately + pass else: content = await _convert_response_content_to_chat_content(input_item.content) message_type = await _get_message_type_by_role(input_item.role) diff --git a/tests/unit/providers/agents/meta_reference/test_openai_responses.py b/tests/unit/providers/agents/meta_reference/test_openai_responses.py index 4132a74a3..3fe1a8970 100644 --- a/tests/unit/providers/agents/meta_reference/test_openai_responses.py +++ b/tests/unit/providers/agents/meta_reference/test_openai_responses.py @@ -24,6 +24,7 @@ from llama_stack.apis.agents.openai_responses import ( OpenAIResponseMessage, OpenAIResponseObjectWithInput, OpenAIResponseOutputMessageContentOutputText, + OpenAIResponseOutputMessageMCPCall, OpenAIResponseOutputMessageWebSearchToolCall, OpenAIResponseText, OpenAIResponseTextFormat, @@ -461,6 +462,53 @@ async def test_prepend_previous_response_web_search(openai_responses_impl, mock_ assert input[3].content == "fake_input" +async def test_prepend_previous_response_mcp_tool_call(openai_responses_impl, mock_responses_store): + """Test prepending a previous response which included an mcp tool call to a new response.""" + input_item_message = OpenAIResponseMessage( + id="123", + content=[OpenAIResponseInputMessageContentText(text="fake_previous_input")], + role="user", + ) + output_tool_call = OpenAIResponseOutputMessageMCPCall( + id="ws_123", + name="fake-tool", + arguments="fake-arguments", + server_label="fake-label", + ) + output_message = OpenAIResponseMessage( + id="123", + content=[OpenAIResponseOutputMessageContentOutputText(text="fake_tool_call_response")], + status="completed", + role="assistant", + ) + response = OpenAIResponseObjectWithInput( + created_at=1, + id="resp_123", + model="fake_model", + output=[output_tool_call, output_message], + status="completed", + text=OpenAIResponseText(format=OpenAIResponseTextFormat(type="text")), + input=[input_item_message], + ) + mock_responses_store.get_response_object.return_value = response + + input_messages = [OpenAIResponseMessage(content="fake_input", role="user")] + input = await openai_responses_impl._prepend_previous_response(input_messages, "resp_123") + + assert len(input) == 4 + # Check for previous input + assert isinstance(input[0], OpenAIResponseMessage) + assert input[0].content[0].text == "fake_previous_input" + # Check for previous output MCP tool call + assert isinstance(input[1], OpenAIResponseOutputMessageMCPCall) + # Check for previous output web search response + assert isinstance(input[2], OpenAIResponseMessage) + assert input[2].content[0].text == "fake_tool_call_response" + # Check for new input + assert isinstance(input[3], OpenAIResponseMessage) + assert input[3].content == "fake_input" + + async def test_create_openai_response_with_instructions(openai_responses_impl, mock_inference_api): # Setup input_text = "What is the capital of Ireland?"