Merge 59abc56345 into 252c71c0b2

litellm/responses/mcp/chat_completions_handler.py

@@ -1,6 +1,8 @@
 """Helpers for handling MCP-aware `/chat/completions` requests."""
 
 import logging
+from collections.abc import Iterator
+from itertools import chain
 from typing import TYPE_CHECKING, Final, cast
 
 from typing_extensions import TypedDict, Unpack
@@ -242,6 +244,8 @@ async def acompletion_with_mcp(
                 self.follow_up_stream = None
                 self.follow_up_iterator = None
                 self.follow_up_exhausted = False
+                self.pending_chunks: Iterator[ModelResponseStream] = iter(())
+                self.any_chunk_yielded = False
 
             def __aiter__(self):
                 return self
@@ -316,9 +320,53 @@ async def acompletion_with_mcp(
                         "Error draining inner MCP stream after final chunk; spend logging may be incomplete"
                     )
 
+            def _is_final_chunk(self, chunk: ModelResponseStream) -> bool:
+                return bool(
+                    hasattr(chunk, "choices")
+                    and chunk.choices
+                    and hasattr(chunk.choices[0], "finish_reason")
+                    and chunk.choices[0].finish_reason is not None
+                )
+
+            def _chunk_has_tool_call_delta(self, chunk: ModelResponseStream) -> bool:
+                choices: Final = getattr(chunk, "choices", None) or ()
+                return any(getattr(getattr(choice, "delta", None), "tool_calls", None) for choice in choices)
+
+            def _yield_chunk(self, chunk: ModelResponseStream) -> ModelResponseStream:
+                if not self.any_chunk_yielded:
+                    self.any_chunk_yielded = True
+                    return self._add_mcp_list_tools_to_chunk(chunk)
+                return chunk
+
+            async def _finish_initial_turn(self):
+                await self._process_tool_calls()
+                if self.tool_results and self.complete_response:
+                    await self._prepare_follow_up_call()
+                # Drain inner stream so CustomStreamWrapper fires its
+                # end-of-stream handler (dispatch_success_handlers →
+                # _ProxyDBLogger → LiteLLM_SpendLogs).  The CSW may
+                # yield one usage chunk before raising StopAsyncIteration.
+                await self._drain_inner_stream()
+
+            def _flush_held_and_final(self, final_chunk: ModelResponseStream) -> ModelResponseStream:
+                flushed_final: Final = self._add_mcp_tool_metadata_to_final_chunk(final_chunk)
+                self.pending_chunks = chain(
+                    (
+                        chunk
+                        for chunk in self.collected_chunks
+                        if chunk is not final_chunk and self._chunk_has_tool_call_delta(chunk)
+                    ),
+                    (flushed_final,),
+                )
+                return self._yield_chunk(next(self.pending_chunks))
+
             async def __anext__(self):
+                pending_chunk: Final = next(self.pending_chunks, None)
+                if pending_chunk is not None:
+                    return self._yield_chunk(pending_chunk)
+
                 # Phase 1: Collect and yield initial stream chunks
-                if not self.stream_exhausted:
+                while not self.stream_exhausted:
                     # Get the iterator from the stream wrapper
                     if not hasattr(self, "_stream_iterator"):
                         self._stream_iterator = self.stream_wrapper.__aiter__()
@@ -330,46 +378,28 @@ async def acompletion_with_mcp(
 
                     try:
                         chunk = await self._stream_iterator.__anext__()
-                        self.collected_chunks.append(chunk)
-
-                        # Add mcp_list_tools to the first chunk
-                        if len(self.collected_chunks) == 1:
-                            chunk = self._add_mcp_list_tools_to_chunk(chunk)
-
-                        # Check if this is the final chunk (has finish_reason)
-                        is_final: Final = (
-                            hasattr(chunk, "choices")
-                            and chunk.choices
-                            and hasattr(chunk.choices[0], "finish_reason")
-                            and chunk.choices[0].finish_reason is not None
-                        )
-
-                        if is_final:
-                            self.stream_exhausted = True
-                            await self._process_tool_calls()
-                            chunk = self._add_mcp_tool_metadata_to_final_chunk(chunk)
-                            if self.tool_results and self.complete_response:
-                                await self._prepare_follow_up_call()
-                            # Drain inner stream so CustomStreamWrapper fires its
-                            # end-of-stream handler (dispatch_success_handlers →
-                            # _ProxyDBLogger → LiteLLM_SpendLogs).  The CSW may
-                            # yield one usage chunk before raising StopAsyncIteration.
-                            await self._drain_inner_stream()
-
-                        return chunk
                     except StopAsyncIteration:
                         self.stream_exhausted = True
-                        # Process tool calls after stream is exhausted
-                        await self._process_tool_calls()
-                        # If we have chunks, yield the final one with metadata
-                        if self.collected_chunks:
-                            final_chunk = self.collected_chunks[-1]
-                            final_chunk = self._add_mcp_tool_metadata_to_final_chunk(final_chunk)
-                            # If we have tool results, prepare follow-up call
-                            if self.tool_results and self.complete_response:
-                                await self._prepare_follow_up_call()
-                            await self._drain_inner_stream()
-                            return final_chunk
+                        if not self.collected_chunks:
+                            break
+                        await self._finish_initial_turn()
+                        if self.follow_up_stream is not None:
+                            break
+                        return self._flush_held_and_final(self.collected_chunks[-1])
+
+                    self.collected_chunks.append(chunk)
+
+                    if self._is_final_chunk(chunk):
+                        self.stream_exhausted = True
+                        await self._finish_initial_turn()
+                        if self.follow_up_stream is not None:
+                            break
+                        return self._flush_held_and_final(chunk)
+
+                    if self._chunk_has_tool_call_delta(chunk):
+                        continue
+
+                    return self._yield_chunk(chunk)
 
                 # Phase 2: Yield follow-up stream chunks if available
                 if self.follow_up_stream and not self.follow_up_exhausted:
@@ -384,7 +414,9 @@ async def acompletion_with_mcp(
                         from litellm._logging import verbose_logger
 
                         verbose_logger.debug("Follow-up chunk yielded: %s", chunk)
-                        return chunk
+                        if self._is_final_chunk(chunk):
+                            chunk = self._add_mcp_tool_metadata_to_final_chunk(chunk)
+                        return self._yield_chunk(chunk)
                     except StopAsyncIteration:
                         self.follow_up_exhausted = True
                         from litellm._logging import verbose_logger

tests/mcp_tests/test_mcp_chat_completions.py

@@ -464,9 +464,9 @@ async def test_completion_mcp_with_streaming_no_timeout_error(monkeypatch):
 async def test_mcp_metadata_in_streaming_final_chunk(monkeypatch):
     """
     Test that MCP metadata is added correctly to streaming chunks:
-    - mcp_list_tools should be in the first chunk
-    - mcp_tool_calls and mcp_call_results should be in the final chunk of initial response
-    - Follow-up response should be streamed after initial response
+    - mcp_list_tools should be in the first chunk yielded to the client
+    - mcp_tool_calls and mcp_call_results should be in the final chunk of the follow-up response
+    - The intermediate tool-call turn must not leak into the client stream (issue #31910)
     """
     from types import SimpleNamespace
     from unittest.mock import patch
@@ -734,95 +734,59 @@ async def test_mcp_metadata_in_streaming_final_chunk(monkeypatch):
             all_chunks = executor.submit(consume_stream).result()
         assert len(all_chunks) > 0, "Should have received streaming chunks"
 
-        # Find chunks from initial response (with tool_calls finish_reason)
-        initial_chunks_list = []
-        follow_up_chunks_list = []
-        for chunk in all_chunks:
-            if hasattr(chunk, "choices") and chunk.choices:
-                choice = chunk.choices[0]
-                if (
-                    hasattr(choice, "finish_reason")
-                    and choice.finish_reason == "tool_calls"
-                ):
-                    initial_chunks_list.append(chunk)
-                elif (
-                    hasattr(choice, "finish_reason") and choice.finish_reason == "stop"
-                ):
-                    follow_up_chunks_list.append(chunk)
-                elif (
-                    not hasattr(choice, "finish_reason") or choice.finish_reason is None
-                ):
-                    # Chunks without finish_reason could be from either stream
-                    # Check if we've seen tool_calls yet
-                    if initial_chunks_list:
-                        follow_up_chunks_list.append(chunk)
-                    else:
-                        initial_chunks_list.append(chunk)
-
-        # Verify initial response chunks
-        assert len(initial_chunks_list) > 0, "Should have initial response chunks"
-
-        # Find the final chunk from initial response (with tool_calls finish_reason)
-        initial_final_chunk = None
-        for chunk in initial_chunks_list:
-            if hasattr(chunk, "choices") and chunk.choices:
-                choice = chunk.choices[0]
-                if (
-                    hasattr(choice, "finish_reason")
-                    and choice.finish_reason == "tool_calls"
-                ):
-                    initial_final_chunk = chunk
-                    break
-
-        if initial_final_chunk is None and initial_chunks_list:
-            initial_final_chunk = initial_chunks_list[-1]
+        finish_reasons = [
+            chunk.choices[0].finish_reason
+            for chunk in all_chunks
+            if chunk.choices and chunk.choices[0].finish_reason is not None
+        ]
+        assert finish_reasons == ["stop"], (
+            f"Intermediate tool-call turn must not leak; expected a single stop. Got: {finish_reasons}"
+        )
+        assert all(
+            not getattr(chunk.choices[0].delta, "tool_calls", None)
+            for chunk in all_chunks
+            if chunk.choices and chunk.choices[0].delta
+        ), "tool_call deltas must not leak into the client stream"
 
+        first_chunk = all_chunks[0]
+        first_provider_fields = getattr(
+            first_chunk.choices[0].delta, "provider_specific_fields", None
+        )
         assert (
-            initial_final_chunk is not None
-        ), "Should have a final chunk from initial response"
+            first_provider_fields is not None
+        ), "First chunk should have provider_specific_fields"
+        assert (
+            "mcp_list_tools" in first_provider_fields
+        ), "First chunk should have mcp_list_tools"
 
-        # Verify mcp_list_tools is in the first chunk of initial response
-        first_chunk = initial_chunks_list[0] if initial_chunks_list else None
-        assert first_chunk is not None, "Should have a first chunk"
-        if hasattr(first_chunk, "choices") and first_chunk.choices:
-            choice = first_chunk.choices[0]
-            if hasattr(choice, "delta") and choice.delta:
-                provider_fields = getattr(
-                    choice.delta, "provider_specific_fields", None
-                )
-                assert (
-                    provider_fields is not None
-                ), "First chunk should have provider_specific_fields"
-                assert (
-                    "mcp_list_tools" in provider_fields
-                ), "First chunk should have mcp_list_tools"
+        final_chunk = all_chunks[-1]
+        assert final_chunk.choices[0].finish_reason == "stop"
+        final_provider_fields = getattr(
+            final_chunk.choices[0].delta, "provider_specific_fields", None
+        )
+        assert (
+            final_provider_fields is not None
+        ), "Final chunk should have provider_specific_fields"
+        assert "mcp_tool_calls" in final_provider_fields, "Should have mcp_tool_calls"
+        assert (
+            "mcp_call_results" in final_provider_fields
+        ), "Should have mcp_call_results"
 
-        # Verify mcp_tool_calls and mcp_call_results are in the final chunk of initial response
-        if hasattr(initial_final_chunk, "choices") and initial_final_chunk.choices:
-            choice = initial_final_chunk.choices[0]
-            if hasattr(choice, "delta") and choice.delta:
-                provider_fields = getattr(
-                    choice.delta, "provider_specific_fields", None
-                )
-                assert (
-                    provider_fields is not None
-                ), "Final chunk should have provider_specific_fields"
-                assert "mcp_tool_calls" in provider_fields, "Should have mcp_tool_calls"
-                assert (
-                    "mcp_call_results" in provider_fields
-                ), "Should have mcp_call_results"
-
-        # Verify follow-up response chunks are present
-        assert len(follow_up_chunks_list) > 0, "Should have follow-up response chunks"
+        content = "".join(
+            chunk.choices[0].delta.content or ""
+            for chunk in all_chunks
+            if chunk.choices and chunk.choices[0].delta
+        )
+        assert content == "Hello world!"
 
 
 @pytest.mark.asyncio
 async def test_mcp_streaming_metadata_ordering(monkeypatch):
     """
     Test that MCP metadata appears in the correct order:
-    - mcp_list_tools should appear in the first chunk (before tool_calls)
-    - mcp_tool_calls and mcp_call_results should appear in the final chunk of initial response
-    - Follow-up response should be streamed after initial response completes
+    - mcp_list_tools should appear in the first chunk yielded to the client
+    - mcp_tool_calls and mcp_call_results should appear in the terminal chunk of the stream
+    - The client stream must contain exactly one terminal finish_reason (issue #31910)
     """
     from types import SimpleNamespace
     from unittest.mock import patch
@@ -1069,66 +1033,38 @@ async def test_mcp_streaming_metadata_ordering(monkeypatch):
             all_chunks = executor.submit(consume_stream).result()
         assert len(all_chunks) > 0, "Should have received streaming chunks"
 
-        # Track when we see each type of metadata
-        mcp_list_tools_seen = False
-        mcp_tool_calls_seen = False
-        mcp_call_results_seen = False
-        tool_calls_finish_reason_seen = False
-        follow_up_content_seen = False
+        finish_reasons = [
+            chunk.choices[0].finish_reason
+            for chunk in all_chunks
+            if chunk.choices and chunk.choices[0].finish_reason is not None
+        ]
+        assert finish_reasons == ["stop"], (
+            f"Client stream must contain exactly one terminal finish_reason. Got: {finish_reasons}"
+        )
 
-        for i, chunk in enumerate(all_chunks):
-            if hasattr(chunk, "choices") and chunk.choices:
-                choice = chunk.choices[0]
-                if hasattr(choice, "delta") and choice.delta:
-                    provider_fields = getattr(
-                        choice.delta, "provider_specific_fields", None
-                    )
-                    if provider_fields:
-                        if "mcp_list_tools" in provider_fields:
-                            mcp_list_tools_seen = True
-                            # mcp_list_tools should appear before tool_calls finish_reason
-                            assert (
-                                not tool_calls_finish_reason_seen
-                            ), "mcp_list_tools should appear before tool_calls finish_reason"
-                        if "mcp_tool_calls" in provider_fields:
-                            mcp_tool_calls_seen = True
-                        if "mcp_call_results" in provider_fields:
-                            mcp_call_results_seen = True
-
-                if (
-                    hasattr(choice, "finish_reason")
-                    and choice.finish_reason == "tool_calls"
-                ):
-                    tool_calls_finish_reason_seen = True
-                    # mcp_tool_calls and mcp_call_results should be in the same chunk as tool_calls finish_reason
-                    if hasattr(choice, "delta") and choice.delta:
-                        provider_fields = getattr(
-                            choice.delta, "provider_specific_fields", None
-                        )
-                        assert provider_fields is not None
-                        assert (
-                            "mcp_tool_calls" in provider_fields
-                        ), "mcp_tool_calls should be in the chunk with tool_calls finish_reason"
-                        assert (
-                            "mcp_call_results" in provider_fields
-                        ), "mcp_call_results should be in the chunk with tool_calls finish_reason"
-
-                if hasattr(choice, "delta") and choice.delta and choice.delta.content:
-                    content = choice.delta.content
-                    if content and (
-                        "Hello" in content or "world" in content or "!" in content
-                    ):
-                        follow_up_content_seen = True
-                        # Follow-up content should appear after tool_calls finish_reason
-                        assert (
-                            tool_calls_finish_reason_seen
-                        ), "Follow-up content should appear after tool_calls finish_reason"
-
-        # Verify all metadata was seen
-        assert mcp_list_tools_seen, "Should have seen mcp_list_tools"
-        assert mcp_tool_calls_seen, "Should have seen mcp_tool_calls"
-        assert mcp_call_results_seen, "Should have seen mcp_call_results"
+        first_provider_fields = getattr(
+            all_chunks[0].choices[0].delta, "provider_specific_fields", None
+        )
         assert (
-            tool_calls_finish_reason_seen
-        ), "Should have seen tool_calls finish_reason"
-        assert follow_up_content_seen, "Should have seen follow-up content"
+            first_provider_fields is not None and "mcp_list_tools" in first_provider_fields
+        ), "mcp_list_tools should be in the first chunk"
+
+        terminal_chunk = all_chunks[-1]
+        assert terminal_chunk.choices[0].finish_reason == "stop"
+        terminal_provider_fields = getattr(
+            terminal_chunk.choices[0].delta, "provider_specific_fields", None
+        )
+        assert terminal_provider_fields is not None
+        assert (
+            "mcp_tool_calls" in terminal_provider_fields
+        ), "mcp_tool_calls should be in the terminal chunk"
+        assert (
+            "mcp_call_results" in terminal_provider_fields
+        ), "mcp_call_results should be in the terminal chunk"
+
+        content = "".join(
+            chunk.choices[0].delta.content or ""
+            for chunk in all_chunks
+            if chunk.choices and chunk.choices[0].delta
+        )
+        assert content == "Hello world!", "Follow-up answer content must reach the client"

tests/test_litellm/responses/mcp/test_chat_completions_handler.py

@@ -749,8 +749,8 @@ async def test_acompletion_with_mcp_streaming_initial_call_is_streaming(monkeypa
 async def test_acompletion_with_mcp_streaming_metadata_in_correct_chunks(monkeypatch):
     """
     Test that MCP metadata is added to the correct chunks:
-    - mcp_list_tools should be in the first chunk
-    - mcp_tool_calls and mcp_call_results should be in the final chunk of initial response
+    - mcp_list_tools should be in the first chunk yielded to the client
+    - mcp_tool_calls and mcp_call_results should be in the final chunk of the follow-up response
     """
     from litellm.utils import CustomStreamWrapper
     from litellm.types.utils import (
@@ -978,35 +978,16 @@ async def test_acompletion_with_mcp_streaming_metadata_in_correct_chunks(monkeyp
             all_chunks.append(chunk)
         assert len(all_chunks) > 0
 
-        # Find first chunk and final chunk from initial response
-        # mcp_list_tools is added to the first chunk (all_chunks[0])
-        first_chunk = all_chunks[0] if all_chunks else None
-        initial_final_chunk = None
+        finish_reasons = [
+            chunk.choices[0].finish_reason
+            for chunk in all_chunks
+            if chunk.choices and chunk.choices[0].finish_reason is not None
+        ]
+        assert finish_reasons == ["stop"], f"Client stream must end with a single stop. Got: {finish_reasons}"
 
-        for chunk in all_chunks:
-            if hasattr(chunk, "choices") and chunk.choices:
-                choice = chunk.choices[0]
-                if (
-                    hasattr(choice, "finish_reason")
-                    and choice.finish_reason == "tool_calls"
-                ):
-                    initial_final_chunk = chunk
-
-        assert first_chunk is not None, "Should have a first chunk"
-        assert (
-            initial_final_chunk is not None
-        ), "Should have a final chunk from initial response"
-
-        # Verify mcp_list_tools is in the first chunk
-        assert (
-            hasattr(first_chunk, "choices") and first_chunk.choices
-        ), "First chunk must have choices"
-        first_choice = first_chunk.choices[0]
-        assert (
-            hasattr(first_choice, "delta") and first_choice.delta
-        ), "First choice must have delta"
+        first_chunk = all_chunks[0]
         first_provider_fields = getattr(
-            first_choice.delta, "provider_specific_fields", None
+            first_chunk.choices[0].delta, "provider_specific_fields", None
         )
         assert (
             first_provider_fields is not None
@@ -1015,16 +996,10 @@ async def test_acompletion_with_mcp_streaming_metadata_in_correct_chunks(monkeyp
             "mcp_list_tools" in first_provider_fields
         ), "First chunk should have mcp_list_tools"
 
-        # Verify mcp_tool_calls and mcp_call_results are in the final chunk of initial response
-        assert (
-            hasattr(initial_final_chunk, "choices") and initial_final_chunk.choices
-        ), "Final chunk must have choices"
-        final_choice = initial_final_chunk.choices[0]
-        assert (
-            hasattr(final_choice, "delta") and final_choice.delta
-        ), "Final choice must have delta"
+        final_chunk = all_chunks[-1]
+        assert final_chunk.choices[0].finish_reason == "stop"
         final_provider_fields = getattr(
-            final_choice.delta, "provider_specific_fields", None
+            final_chunk.choices[0].delta, "provider_specific_fields", None
         )
         assert (
             final_provider_fields is not None
@@ -1353,6 +1328,448 @@ async def test_acompletion_with_mcp_streaming_drains_inner_stream_after_exhausti
     assert initial_stream.drained_after_exhaustion is True
 
 
+def _create_mcp_stream_chunk(content, finish_reason=None, tool_calls=None):
+    from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
+
+    return ModelResponseStream(
+        id="test-stream",
+        model="test-model",
+        created=1234567890,
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                index=0,
+                delta=Delta(content=content, role="assistant", tool_calls=tool_calls),
+                finish_reason=finish_reason,
+            )
+        ],
+    )
+
+
+def _make_mock_stream_class(stream_chunks):
+    from unittest.mock import MagicMock
+
+    from litellm.utils import CustomStreamWrapper
+
+    logging_obj = MagicMock()
+    logging_obj.model_call_details = {}
+
+    class MockStream(CustomStreamWrapper):
+        def __init__(self):
+            super().__init__(
+                completion_stream=None,
+                model="test-model",
+                logging_obj=logging_obj,
+            )
+            self.chunks = stream_chunks
+            self._index = 0
+
+        def __aiter__(self):
+            return self
+
+        async def __anext__(self):
+            if self._index < len(self.chunks):
+                chunk = self.chunks[self._index]
+                self._index += 1
+                return chunk
+            raise StopAsyncIteration
+
+    return MockStream
+
+
+def _patch_mcp_auto_exec_scaffolding(monkeypatch, tools, openai_tools, tool_calls, tool_results):
+    monkeypatch.setattr(
+        LiteLLM_Proxy_MCP_Handler,
+        "_should_use_litellm_mcp_gateway",
+        staticmethod(lambda tools: True),
+    )
+    monkeypatch.setattr(
+        LiteLLM_Proxy_MCP_Handler,
+        "_parse_mcp_tools",
+        staticmethod(lambda tools: (tools, [])),
+    )
+
+    async def mock_process(**_):
+        return (tools, {"local_search": "local"})
+
+    monkeypatch.setattr(
+        LiteLLM_Proxy_MCP_Handler,
+        "_process_mcp_tools_without_openai_transform",
+        mock_process,
+    )
+    monkeypatch.setattr(
+        LiteLLM_Proxy_MCP_Handler,
+        "_transform_mcp_tools_to_openai",
+        staticmethod(lambda *_, **__: openai_tools),
+    )
+    monkeypatch.setattr(
+        LiteLLM_Proxy_MCP_Handler,
+        "_should_auto_execute_tools",
+        staticmethod(lambda **_: True),
+    )
+    monkeypatch.setattr(
+        LiteLLM_Proxy_MCP_Handler,
+        "_extract_tool_calls_from_chat_response",
+        staticmethod(lambda **_: tool_calls),
+    )
+
+    async def mock_execute(**_):
+        return tool_results
+
+    monkeypatch.setattr(
+        LiteLLM_Proxy_MCP_Handler,
+        "_execute_tool_calls",
+        mock_execute,
+    )
+    monkeypatch.setattr(
+        LiteLLM_Proxy_MCP_Handler,
+        "_create_follow_up_messages_for_chat",
+        staticmethod(
+            lambda **_: [
+                {"role": "user", "content": "hello"},
+                {
+                    "role": "assistant",
+                    "tool_calls": [
+                        {
+                            "id": "call-1",
+                            "type": "function",
+                            "function": {"name": "local_search", "arguments": "{}"},
+                        }
+                    ],
+                },
+                {
+                    "role": "tool",
+                    "tool_call_id": "call-1",
+                    "name": "local_search",
+                    "content": "executed",
+                },
+            ]
+        ),
+    )
+    monkeypatch.setattr(
+        ResponsesAPIRequestUtils,
+        "extract_mcp_headers_from_request",
+        staticmethod(lambda **_: (None, None, None, None)),
+    )
+
+
+@pytest.mark.asyncio
+async def test_acompletion_with_mcp_streaming_suppresses_intermediate_tool_call_turn(monkeypatch):
+    """
+    Regression test for https://github.com/BerriAI/litellm/issues/31910:
+    when the proxy auto-executes MCP tools with stream=True, the intermediate
+    tool-call turn (raw tool_call deltas + finish_reason "tool_calls") must not
+    leak into the client stream. The client should see a single assistant
+    message ending with exactly one terminal finish_reason.
+    """
+    from litellm.types.utils import ChatCompletionDeltaToolCall, Function
+
+    tools = [{"type": "mcp", "server_url": "litellm_proxy/mcp/local"}]
+    openai_tools = [{"type": "function", "function": {"name": "local_search"}}]
+    tool_calls = [
+        {
+            "id": "call-1",
+            "type": "function",
+            "function": {"name": "local_search", "arguments": "{}"},
+        }
+    ]
+    tool_results = [{"tool_call_id": "call-1", "result": "executed"}]
+
+    initial_chunks = [
+        _create_mcp_stream_chunk("Let me check. "),
+        _create_mcp_stream_chunk(
+            None,
+            tool_calls=[
+                ChatCompletionDeltaToolCall(
+                    id="call-1",
+                    type="function",
+                    function=Function(name="local_search", arguments="{}"),
+                    index=0,
+                )
+            ],
+        ),
+        _create_mcp_stream_chunk("", finish_reason="tool_calls"),
+    ]
+    follow_up_chunks = [
+        _create_mcp_stream_chunk("Hello"),
+        _create_mcp_stream_chunk(" world", finish_reason="stop"),
+    ]
+
+    InitialStream = _make_mock_stream_class(initial_chunks)
+    FollowUpStream = _make_mock_stream_class(follow_up_chunks)
+
+    async def mock_acompletion(**kwargs):
+        messages = kwargs.get("messages", [])
+        is_follow_up = any(
+            isinstance(msg, dict) and msg.get("role") == "tool" for msg in messages
+        )
+        return FollowUpStream() if is_follow_up else InitialStream()
+
+    mock_acompletion_func = AsyncMock(side_effect=mock_acompletion)
+    _patch_mcp_auto_exec_scaffolding(monkeypatch, tools, openai_tools, tool_calls, tool_results)
+
+    with patch("litellm.acompletion", mock_acompletion_func):  # test-quality-ok: [TQ008] Supply exact model chunks to exercise MCP stream orchestration
+        result = await acompletion_with_mcp(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": "hello"}],
+            tools=tools,
+            stream=True,
+        )
+
+        all_chunks = []
+        async for chunk in result:
+            all_chunks.append(chunk)
+
+    assert all(
+        not getattr(chunk.choices[0].delta, "tool_calls", None) for chunk in all_chunks if chunk.choices
+    ), f"tool_call deltas must not leak into the client stream. Got: {all_chunks}"
+
+    finish_reasons = [
+        chunk.choices[0].finish_reason
+        for chunk in all_chunks
+        if chunk.choices and chunk.choices[0].finish_reason is not None
+    ]
+    assert finish_reasons == ["stop"], f"Expected a single terminal stop. Got: {finish_reasons}"
+    assert all_chunks[-1].choices[0].finish_reason == "stop"
+
+    content = "".join(
+        chunk.choices[0].delta.content or "" for chunk in all_chunks if chunk.choices and chunk.choices[0].delta
+    )
+    assert content == "Let me check. Hello world"
+
+
+@pytest.mark.asyncio
+async def test_acompletion_with_mcp_streaming_flushes_tool_call_turn_when_no_follow_up(monkeypatch):
+    """
+    When tool execution produces no results (so no follow-up stream is created),
+    the held tool-call chunks and the finish_reason "tool_calls" chunk must be
+    flushed so the client still receives a complete, terminated stream.
+    """
+    from litellm.types.utils import ChatCompletionDeltaToolCall, Function
+
+    tools = [{"type": "mcp", "server_url": "litellm_proxy/mcp/local"}]
+    openai_tools = [{"type": "function", "function": {"name": "local_search"}}]
+    tool_calls = [
+        {
+            "id": "call-1",
+            "type": "function",
+            "function": {"name": "local_search", "arguments": "{}"},
+        }
+    ]
+
+    initial_chunks = [
+        _create_mcp_stream_chunk(
+            None,
+            tool_calls=[
+                ChatCompletionDeltaToolCall(
+                    id="call-1",
+                    type="function",
+                    function=Function(name="local_search", arguments="{}"),
+                    index=0,
+                )
+            ],
+        ),
+        _create_mcp_stream_chunk("", finish_reason="tool_calls"),
+    ]
+
+    InitialStream = _make_mock_stream_class(initial_chunks)
+
+    async def mock_acompletion(**kwargs):
+        return InitialStream()
+
+    mock_acompletion_func = AsyncMock(side_effect=mock_acompletion)
+    _patch_mcp_auto_exec_scaffolding(monkeypatch, tools, openai_tools, tool_calls, tool_results=[])
+
+    with patch("litellm.acompletion", mock_acompletion_func):  # test-quality-ok: [TQ008] Supply exact model chunks to exercise MCP stream orchestration
+        result = await acompletion_with_mcp(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": "hello"}],
+            tools=tools,
+            stream=True,
+        )
+
+        all_chunks = []
+        async for chunk in result:
+            all_chunks.append(chunk)
+
+    assert any(
+        getattr(chunk.choices[0].delta, "tool_calls", None) for chunk in all_chunks if chunk.choices
+    ), "Held tool-call chunks must be flushed when no follow-up stream is created"
+    assert all_chunks[-1].choices[0].finish_reason == "tool_calls"
+
+
+@pytest.mark.asyncio
+async def test_acompletion_with_mcp_streaming_no_duplicate_chunk_on_abrupt_termination(monkeypatch):
+    """
+    Regression test for a duplicate-chunk bug in the abrupt-termination fallback:
+    when the initial stream ends via StopAsyncIteration without a finish_reason
+    chunk and the last collected chunk is a held tool-call delta, that chunk is
+    both in held_tool_call_chunks and collected_chunks[-1]. It must be yielded
+    to the client exactly once.
+    """
+    from litellm.types.utils import ChatCompletionDeltaToolCall, Function
+
+    tools = [{"type": "mcp", "server_url": "litellm_proxy/mcp/local"}]
+    openai_tools = [{"type": "function", "function": {"name": "local_search"}}]
+    tool_calls = [
+        {
+            "id": "call-1",
+            "type": "function",
+            "function": {"name": "local_search", "arguments": "{}"},
+        }
+    ]
+
+    initial_chunks = [
+        _create_mcp_stream_chunk("partial answer "),
+        _create_mcp_stream_chunk(
+            None,
+            tool_calls=[
+                ChatCompletionDeltaToolCall(
+                    id="call-1",
+                    type="function",
+                    function=Function(name="local_search", arguments="{}"),
+                    index=0,
+                )
+            ],
+        ),
+    ]
+
+    InitialStream = _make_mock_stream_class(initial_chunks)
+
+    async def mock_acompletion(**kwargs):
+        return InitialStream()
+
+    mock_acompletion_func = AsyncMock(side_effect=mock_acompletion)
+    _patch_mcp_auto_exec_scaffolding(monkeypatch, tools, openai_tools, tool_calls, tool_results=[])
+
+    with patch("litellm.acompletion", mock_acompletion_func):  # test-quality-ok: [TQ008] Supply exact model chunks to exercise MCP stream orchestration
+        result = await acompletion_with_mcp(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": "hello"}],
+            tools=tools,
+            stream=True,
+        )
+
+        all_chunks = []
+        async for chunk in result:
+            all_chunks.append(chunk)
+
+    tool_call_chunk_count = sum(
+        1 for chunk in all_chunks if chunk.choices and getattr(chunk.choices[0].delta, "tool_calls", None)
+    )
+    assert tool_call_chunk_count == 1, (
+        f"The held tool-call chunk must be yielded exactly once on abrupt termination. Got chunks: {all_chunks}"
+    )
+    assert len(all_chunks) == len(set(id(chunk) for chunk in all_chunks)), "No chunk object may be yielded twice"
+
+
+@pytest.mark.asyncio
+async def test_acompletion_with_mcp_streaming_abrupt_termination_with_follow_up_suppresses_tool_turn(monkeypatch):
+    """
+    When the initial stream ends via StopAsyncIteration without ever emitting a
+    finish_reason chunk but tool execution still succeeds, the held tool-call
+    chunks must be suppressed and the follow-up answer streamed, same as the
+    clean-termination path.
+    """
+    from litellm.types.utils import ChatCompletionDeltaToolCall, Function
+
+    tools = [{"type": "mcp", "server_url": "litellm_proxy/mcp/local"}]
+    openai_tools = [{"type": "function", "function": {"name": "local_search"}}]
+    tool_calls = [
+        {
+            "id": "call-1",
+            "type": "function",
+            "function": {"name": "local_search", "arguments": "{}"},
+        }
+    ]
+    tool_results = [{"tool_call_id": "call-1", "result": "executed"}]
+
+    initial_chunks = [
+        _create_mcp_stream_chunk(
+            None,
+            tool_calls=[
+                ChatCompletionDeltaToolCall(
+                    id="call-1",
+                    type="function",
+                    function=Function(name="local_search", arguments="{}"),
+                    index=0,
+                )
+            ],
+        ),
+    ]
+    follow_up_chunks = [
+        _create_mcp_stream_chunk("Hello"),
+        _create_mcp_stream_chunk(" world", finish_reason="stop"),
+    ]
+
+    InitialStream = _make_mock_stream_class(initial_chunks)
+    FollowUpStream = _make_mock_stream_class(follow_up_chunks)
+
+    async def mock_acompletion(**kwargs):
+        messages = kwargs.get("messages", [])
+        is_follow_up = any(
+            isinstance(msg, dict) and msg.get("role") == "tool" for msg in messages
+        )
+        return FollowUpStream() if is_follow_up else InitialStream()
+
+    mock_acompletion_func = AsyncMock(side_effect=mock_acompletion)
+    _patch_mcp_auto_exec_scaffolding(monkeypatch, tools, openai_tools, tool_calls, tool_results)
+
+    with patch("litellm.acompletion", mock_acompletion_func):  # test-quality-ok: [TQ008] Supply exact model chunks to exercise MCP stream orchestration
+        result = await acompletion_with_mcp(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": "hello"}],
+            tools=tools,
+            stream=True,
+        )
+
+        all_chunks = []
+        async for chunk in result:
+            all_chunks.append(chunk)
+
+    assert all(
+        not getattr(chunk.choices[0].delta, "tool_calls", None) for chunk in all_chunks if chunk.choices
+    ), f"tool_call deltas must not leak even when the initial stream ends abruptly. Got: {all_chunks}"
+    finish_reasons = [
+        chunk.choices[0].finish_reason
+        for chunk in all_chunks
+        if chunk.choices and chunk.choices[0].finish_reason is not None
+    ]
+    assert finish_reasons == ["stop"], f"Expected a single terminal stop. Got: {finish_reasons}"
+    content = "".join(
+        chunk.choices[0].delta.content or "" for chunk in all_chunks if chunk.choices and chunk.choices[0].delta
+    )
+    assert content == "Hello world"
+
+
+@pytest.mark.asyncio
+async def test_acompletion_with_mcp_streaming_empty_initial_stream_terminates_cleanly(monkeypatch):
+    tools = [{"type": "mcp", "server_url": "litellm_proxy/mcp/local"}]
+    openai_tools = [{"type": "function", "function": {"name": "local_search"}}]
+
+    InitialStream = _make_mock_stream_class([])
+
+    async def mock_acompletion(**kwargs):
+        return InitialStream()
+
+    mock_acompletion_func = AsyncMock(side_effect=mock_acompletion)
+    _patch_mcp_auto_exec_scaffolding(monkeypatch, tools, openai_tools, tool_calls=[], tool_results=[])
+
+    with patch("litellm.acompletion", mock_acompletion_func):  # test-quality-ok: [TQ008] Supply exact model chunks to exercise MCP stream orchestration
+        result = await acompletion_with_mcp(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": "hello"}],
+            tools=tools,
+            stream=True,
+        )
+
+        all_chunks = []
+        async for chunk in result:
+            all_chunks.append(chunk)
+
+    assert all_chunks == [], "An empty initial stream must terminate cleanly with no chunks"
+
+
 @pytest.mark.asyncio
 @respx.mock
 async def test_acompletion_with_mcp_forwards_unserved_external_mcp_tool_to_the_provider(monkeypatch):