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fix(anthropic): preserve MCP tool results in the non-Anthropic Messages bridge (#42783)
The tool_result user message built by the /v1/messages MCP loop used tuple content, which the Messages to Chat Completions adapter silently dropped, so non-Anthropic models re-requested the tool until the iteration cap or the provider rejected the follow-up. Emit list content so the existing tool_result branch translates it into a role tool message keyed by tool_call_id. Resolves LIT-8474 Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: bot_apk <apk@cognition.ai>
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devin-ai-integration[bot]
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commit
5db8543817
3 changed files with 62 additions and 41 deletions
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@ -50,14 +50,14 @@ def _build_tool_result_message(tool_results: Sequence[Mapping[str, object]]) ->
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"""Turn executed tool results into the user message Anthropic expects."""
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return AnthropicMessagesUserMessageParam(
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role="user",
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content=tuple(
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content=[
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AnthropicMessagesToolResultParam(
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type="tool_result",
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tool_use_id=str(result.get("tool_call_id") or ""),
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content=str(result.get("result") or ""),
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)
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for result in tool_results
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),
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],
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)
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@ -258,16 +258,6 @@ def _peer_add_calls(peer: McpPeer) -> tuple[dict[str, object], ...]:
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)
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def _skip_if_bridge_drops_tool_result(
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rig: Rig, requests: tuple[tuple[str, ...], ...], calls: tuple[object, ...]
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) -> None:
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if rig.surface == "messages_bridge" and len(calls) > 1 and len(requests) > 2:
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pytest.skip(
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"BUG: /v1/messages MCP tool loop over a non-Anthropic model drops the tool_result message, "
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"so the tool is re-executed until the iteration cap"
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)
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@pytest.mark.parametrize("surface", SURFACES)
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def test_auto_approved_gateway_tool_is_listed_executed_once_and_fed_back(gateway: Gateway, surface: Surface) -> None:
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with _rig(gateway, surface) as rig:
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@ -276,7 +266,6 @@ def test_auto_approved_gateway_tool_is_listed_executed_once_and_fed_back(gateway
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assert response.status_code == 200, response.text
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calls: Final = _peer_add_calls(rig.peer)
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requests: Final = rig.upstream_tools()
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_skip_if_bridge_drops_tool_result(rig, requests, calls)
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assert [call["body"]["params"]["name"] for call in calls] == ["add"], calls
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assert calls[0]["body"]["params"]["arguments"] == ADD, calls
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assert len(requests) == 2, requests
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@ -3,7 +3,9 @@ from unittest.mock import AsyncMock, patch
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import pytest
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from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import (
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LiteLLMAnthropicMessagesAdapter,
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)
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from litellm.llms.anthropic.experimental_pass_through.messages.handler import (
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anthropic_messages_handler,
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)
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@ -59,7 +61,7 @@ def test_anthropic_messages_handler_skips_the_gateway_on_recursion():
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"litellm.llms.anthropic.experimental_pass_through.messages.mcp_handler.anthropic_messages_with_mcp",
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new=AsyncMock(return_value={"routed": True}),
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) as routed:
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with pytest.raises(ValueError, match='anthropic_messages_handler is not implemented for sync calls'):
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with pytest.raises(ValueError, match="anthropic_messages_handler is not implemented for sync calls"):
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anthropic_messages_handler(
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max_tokens=100,
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messages=[{"role": "user", "content": "hi"}],
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@ -78,7 +80,7 @@ def test_anthropic_messages_handler_leaves_native_tools_alone():
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"litellm.llms.anthropic.experimental_pass_through.messages.mcp_handler.anthropic_messages_with_mcp",
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new=AsyncMock(return_value={"routed": True}),
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) as routed:
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with pytest.raises(ValueError, match='anthropic_messages_handler is not implemented for sync calls'):
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with pytest.raises(ValueError, match="anthropic_messages_handler is not implemented for sync calls"):
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anthropic_messages_handler(
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max_tokens=100,
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messages=[{"role": "user", "content": "hi"}],
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@ -115,8 +117,31 @@ def test_build_tool_result_message_uses_anthropic_tool_result_blocks():
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message = _build_tool_result_message([{"tool_call_id": "toolu_1", "result": "9 sections", "name": "read_wiki"}])
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assert message["role"] == "user"
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assert list(message["content"]) == [
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{"type": "tool_result", "tool_use_id": "toolu_1", "content": "9 sections"}
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assert message["content"] == [{"type": "tool_result", "tool_use_id": "toolu_1", "content": "9 sections"}]
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def test_build_tool_result_message_survives_the_chat_completions_bridge():
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"""
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Regression test (LIT-8474): a non-Anthropic model behind /v1/messages must see
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the executed tool result as a role="tool" message keyed by the tool_call_id.
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The bridge only translates list content, so a tuple-shaped user message was
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dropped and the model re-requested the tool until the iteration cap.
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"""
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message = _build_tool_result_message(
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[
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{"tool_call_id": "call_1", "result": "5", "name": "add"},
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{"tool_call_id": "call_2", "result": "7", "name": "add"},
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]
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)
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translated = LiteLLMAnthropicMessagesAdapter().translate_anthropic_messages_to_openai(
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[message], model="hosted_vllm/gpt-4o-mini", custom_llm_provider="hosted_vllm"
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)
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assert translated == [
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{"role": "tool", "tool_call_id": "call_1", "content": "5"},
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{"role": "tool", "tool_call_id": "call_2", "content": "7"},
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]
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@ -157,19 +182,23 @@ async def test_anthropic_messages_with_mcp_forwards_the_callers_mcp_credentials(
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{"stop_reason": "end_turn", "content": [{"type": "text", "text": "done"}]},
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]
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with patch.object(MCPRequestContext, "resolve", return_value=context), patch.object(
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mcp_handler.LiteLLM_Proxy_MCP_Handler
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if hasattr(mcp_handler, "LiteLLM_Proxy_MCP_Handler")
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else __import__(
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"litellm.responses.mcp.litellm_proxy_mcp_handler", fromlist=["LiteLLM_Proxy_MCP_Handler"]
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).LiteLLM_Proxy_MCP_Handler,
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"_process_mcp_tools_without_openai_transform",
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new=process,
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), patch.object(
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import_module("litellm.responses.mcp.litellm_proxy_mcp_handler").LiteLLM_Proxy_MCP_Handler, "_execute_tool_calls",
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new=execute,
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), patch(
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"litellm.anthropic_messages", new=AsyncMock(side_effect=responses)
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with (
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patch.object(MCPRequestContext, "resolve", return_value=context),
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patch.object(
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mcp_handler.LiteLLM_Proxy_MCP_Handler
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if hasattr(mcp_handler, "LiteLLM_Proxy_MCP_Handler")
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else __import__(
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"litellm.responses.mcp.litellm_proxy_mcp_handler", fromlist=["LiteLLM_Proxy_MCP_Handler"]
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).LiteLLM_Proxy_MCP_Handler,
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"_process_mcp_tools_without_openai_transform",
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new=process,
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),
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patch.object(
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import_module("litellm.responses.mcp.litellm_proxy_mcp_handler").LiteLLM_Proxy_MCP_Handler,
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"_execute_tool_calls",
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new=execute,
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),
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patch("litellm.anthropic_messages", new=AsyncMock(side_effect=responses)),
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):
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await mcp_handler.anthropic_messages_with_mcp(
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max_tokens=100,
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@ -220,16 +249,19 @@ async def test_anthropic_messages_with_mcp_stops_when_every_tool_call_is_skipped
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}
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anthropic_messages_mock = AsyncMock(return_value=tool_use_response)
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with patch.object(
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MCPRequestContext, "resolve", return_value=MCPRequestContext(user_api_key_auth="auth")
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), patch.object(
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import_module("litellm.responses.mcp.litellm_proxy_mcp_handler").LiteLLM_Proxy_MCP_Handler, "_process_mcp_tools_without_openai_transform",
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new=AsyncMock(return_value=([], {})),
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), patch.object(
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import_module("litellm.responses.mcp.litellm_proxy_mcp_handler").LiteLLM_Proxy_MCP_Handler, "_execute_tool_calls",
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new=AsyncMock(return_value=[]),
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), patch(
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"litellm.anthropic_messages", new=anthropic_messages_mock
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with (
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patch.object(MCPRequestContext, "resolve", return_value=MCPRequestContext(user_api_key_auth="auth")),
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patch.object(
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import_module("litellm.responses.mcp.litellm_proxy_mcp_handler").LiteLLM_Proxy_MCP_Handler,
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"_process_mcp_tools_without_openai_transform",
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new=AsyncMock(return_value=([], {})),
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),
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patch.object(
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import_module("litellm.responses.mcp.litellm_proxy_mcp_handler").LiteLLM_Proxy_MCP_Handler,
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"_execute_tool_calls",
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new=AsyncMock(return_value=[]),
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),
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patch("litellm.anthropic_messages", new=anthropic_messages_mock),
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):
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result = await mcp_handler.anthropic_messages_with_mcp(
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max_tokens=100,
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