mirror of
https://github.com/BerriAI/litellm.git
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Merge 4982ead0e7 into e52f05566d
This commit is contained in:
ArjunPakhan
GitHub
commit
51b435e5fb
2 changed files with 175 additions and 4 deletions
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@ -60,6 +60,23 @@ def _build_tool_result_message(tool_results: Sequence[Mapping[str, object]]) ->
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)
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def _has_client_side_tool(
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tool_use_blocks: Sequence[Mapping[str, object]],
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other_tools: Sequence[Mapping[str, object]] | None,
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tool_server_map: Mapping[str, str],
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) -> bool:
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client_tool_names: Final = tuple(
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t.get("name") for t in (other_tools or ()) if isinstance(t, dict) and isinstance(t.get("name"), str)
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) # kwargs-ok: extract client tool names tuple
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for block in tool_use_blocks:
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name = block.get("name") # kwargs-ok: extract block tool name
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if name in client_tool_names or (
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bool(tool_server_map) and name not in tool_server_map
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): # kwargs-ok: map membership test
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return True
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return False
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async def anthropic_messages_with_mcp(
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max_tokens: int,
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messages: Sequence[Mapping[str, object]],
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@ -89,7 +106,7 @@ async def anthropic_messages_with_mcp(
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max_tokens=max_tokens,
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messages=list(messages),
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model=model,
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tools=list(tools) if tools else None,
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tools=list(tools) if tools else None, # kwargs-ok: pass tools list
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_skip_mcp_handler=True,
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**kwargs,
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)
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@ -144,6 +161,9 @@ async def anthropic_messages_with_mcp(
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if not tool_use_blocks:
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break
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if _has_client_side_tool(tool_use_blocks, other_tools, tool_server_map):
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break
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tool_results = await LiteLLM_Proxy_MCP_Handler._execute_tool_calls(
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tool_server_map=tool_server_map,
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tool_calls=list(tool_use_blocks),
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@ -157,12 +177,10 @@ async def anthropic_messages_with_mcp(
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request_tags=list(context.request_tags) if context.request_tags else None,
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)
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# Every tool call was skipped, so there is nothing to feed back; a
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# tool_result message with empty content is rejected by Anthropic.
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if not tool_results:
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break
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working_messages = (
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working_messages = ( # rebind-ok: append assistant and tool_result turns to working context
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*working_messages,
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{"role": "assistant", "content": list(_get_response_content(response))},
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_build_tool_result_message(tool_results),
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153
tests/test_mcp_client_tool_passthrough.py
Normal file
153
tests/test_mcp_client_tool_passthrough.py
Normal file
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@ -0,0 +1,153 @@
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from types import SimpleNamespace
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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from litellm.llms.anthropic.experimental_pass_through.messages.mcp_handler import (
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anthropic_messages_with_mcp,
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)
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@pytest.mark.asyncio
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async def test_mcp_auto_execute_bypasses_client_side_tools():
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"""
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Ensure that if a response contains client-native tools (not present in tool_server_map),
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the auto-execution loop breaks early and passes the response back to the client.
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"""
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mock_mcp_references = [{"type": "mcp", "server_url": "http://localhost/mcp", "require_approval": "never"}]
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client_tools = [{"name": "Read", "description": "Client Read tool"}]
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mock_mcp_tools = [SimpleNamespace(name="mcp_tool_1", description="MCP Tool", inputSchema={"type": "object"})]
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mock_tool_server_map = {"mcp_tool_1": "http://localhost/mcp"}
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mock_anthropic_response = {
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"id": "msg_123",
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"type": "message",
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"role": "assistant",
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"content": [
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{"type": "tool_use", "id": "call_mcp", "name": "mcp_tool_1", "input": {}},
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{
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"type": "tool_use",
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"id": "call_client",
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"name": "Read",
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"input": {"file_path": "test.txt"},
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},
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],
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"stop_reason": "tool_use",
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}
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mock_context = MagicMock()
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mock_context.user_api_key_auth = None
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mock_context.litellm_trace_id = "trace_123"
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mock_context.mcp_auth_header = None
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mock_context.mcp_server_auth_headers = None
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mock_context.request_tags = None
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mock_context.oauth2_headers = None
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mock_context.raw_headers = None
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mock_context.litellm_call_id = "call_123"
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path_resolve = "litellm.responses.mcp.request_context.MCPRequestContext.resolve"
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path_parse = "litellm.responses.mcp.litellm_proxy_mcp_handler.LiteLLM_Proxy_MCP_Handler._parse_mcp_tools"
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path_process = (
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"litellm.responses.mcp.litellm_proxy_mcp_handler."
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"LiteLLM_Proxy_MCP_Handler._process_mcp_tools_without_openai_transform"
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)
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path_auto = "litellm.responses.mcp.litellm_proxy_mcp_handler.LiteLLM_Proxy_MCP_Handler._should_auto_execute_tools"
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path_exec = "litellm.responses.mcp.litellm_proxy_mcp_handler.LiteLLM_Proxy_MCP_Handler._execute_tool_calls"
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path_call = "litellm.llms.anthropic.experimental_pass_through.messages.mcp_handler._AnthropicMessagesCall"
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with (
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patch(path_resolve, return_value=mock_context),
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patch(path_parse, return_value=(mock_mcp_references, client_tools)),
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patch(path_process, new_callable=AsyncMock) as mock_process,
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patch(path_auto, return_value=True),
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patch(path_exec, new_callable=AsyncMock) as mock_execute,
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patch(path_call) as mock_call,
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):
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mock_process.return_value = (mock_mcp_tools, mock_tool_server_map)
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mock_fn = AsyncMock(return_value=mock_anthropic_response)
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mock_call.return_value.fn = mock_fn
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response = await anthropic_messages_with_mcp(
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max_tokens=100,
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messages=[{"role": "user", "content": "Read test.txt and run image_understand"}],
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model="claude-3-5-sonnet-20241022",
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tools=[*mock_mcp_references, *client_tools],
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)
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mock_execute.assert_not_called()
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assert response == mock_anthropic_response
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@pytest.mark.asyncio
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async def test_mcp_auto_execute_runs_server_side_mcp_tools():
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"""
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Ensure that if a response contains ONLY server-side MCP tools,
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auto-execution proceeds as expected and returns False for client-side tool check.
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"""
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mock_mcp_references = [{"type": "mcp", "server_url": "http://localhost/mcp", "require_approval": "never"}]
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mock_mcp_tools = [SimpleNamespace(name="mcp_tool_1", description="MCP Tool", inputSchema={"type": "object"})]
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mock_tool_server_map = {"mcp_tool_1": "http://localhost/mcp"}
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mock_anthropic_response = {
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"id": "msg_123",
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"type": "message",
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"role": "assistant",
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"content": [
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{"type": "tool_use", "id": "call_mcp", "name": "mcp_tool_1", "input": {}},
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],
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"stop_reason": "tool_use",
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}
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mock_context = MagicMock()
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mock_context.user_api_key_auth = None
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mock_context.litellm_trace_id = "trace_123"
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mock_context.mcp_auth_header = None
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mock_context.mcp_server_auth_headers = None
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mock_context.request_tags = None
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mock_context.oauth2_headers = None
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mock_context.raw_headers = None
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mock_context.litellm_call_id = "call_123"
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path_resolve = "litellm.responses.mcp.request_context.MCPRequestContext.resolve"
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path_parse = "litellm.responses.mcp.litellm_proxy_mcp_handler.LiteLLM_Proxy_MCP_Handler._parse_mcp_tools"
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path_process = (
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"litellm.responses.mcp.litellm_proxy_mcp_handler."
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"LiteLLM_Proxy_MCP_Handler._process_mcp_tools_without_openai_transform"
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)
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path_auto = "litellm.responses.mcp.litellm_proxy_mcp_handler.LiteLLM_Proxy_MCP_Handler._should_auto_execute_tools"
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path_exec = "litellm.responses.mcp.litellm_proxy_mcp_handler.LiteLLM_Proxy_MCP_Handler._execute_tool_calls"
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path_call = "litellm.llms.anthropic.experimental_pass_through.messages.mcp_handler._AnthropicMessagesCall"
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with (
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patch(path_resolve, return_value=mock_context),
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patch(path_parse, return_value=(mock_mcp_references, [])),
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patch(path_process, new_callable=AsyncMock) as mock_process,
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patch(path_auto, return_value=True),
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patch(path_exec, new_callable=AsyncMock) as mock_execute,
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patch(path_call) as mock_call,
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):
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mock_process.return_value = (mock_mcp_tools, mock_tool_server_map)
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mock_execute.return_value = [{"tool_call_id": "call_mcp", "result": "ok"}]
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mock_final_response = {
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"id": "msg_124",
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"type": "message",
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"role": "assistant",
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"content": [{"type": "text", "text": "done"}],
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"stop_reason": "end_turn",
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}
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mock_fn = AsyncMock(side_effect=[mock_anthropic_response, mock_final_response])
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mock_call.return_value.fn = mock_fn
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response = await anthropic_messages_with_mcp(
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max_tokens=100,
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messages=[{"role": "user", "content": "run mcp_tool_1"}],
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model="claude-3-5-sonnet-20241022",
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tools=mock_mcp_references,
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)
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mock_execute.assert_called_once()
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assert response == mock_final_response
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