test(mcp): add server-side execution test case for 100% codecov coverage

litellm/llms/anthropic/experimental_pass_through/messages/mcp_handler.py

@@ -64,17 +64,17 @@ def _has_client_side_tool(
     tool_use_blocks: Sequence[Mapping[str, object]],
     other_tools: Sequence[Mapping[str, object]] | None,
     tool_server_map: Mapping[str, str],
-) -> bool:  # kwargs-ok: helper function inspecting tool blocks for client passthrough
+) -> bool:  # kwargs-ok: helper inspecting tool blocks for client passthrough
     client_tool_names = {
-        t.get("name")  # kwargs-ok: extract client tool name
+        t.get("name")  # kwargs-ok: extract client tool name from dictionary
         for t in (other_tools or ())
         if isinstance(t, dict) and t.get("name")  # kwargs-ok: extract client tool name
     }
     for block in tool_use_blocks:
-        name = block.get("name")  # kwargs-ok: extract block tool name
+        name = block.get("name")  # kwargs-ok: extract block tool name from dictionary
         if name in client_tool_names or (
             bool(tool_server_map) and name not in tool_server_map
-        ):  # kwargs-ok: check map membership
+        ):  # kwargs-ok: map membership test
             return True
     return False
 

tests/test_mcp_client_tool_passthrough.py

@@ -77,3 +77,77 @@ async def test_mcp_auto_execute_bypasses_client_side_tools():
 
         mock_execute.assert_not_called()
         assert response == mock_anthropic_response
+
+
+@pytest.mark.asyncio
+async def test_mcp_auto_execute_runs_server_side_mcp_tools():
+    """
+    Ensure that if a response contains ONLY server-side MCP tools,
+    auto-execution proceeds as expected and returns False for client-side tool check.
+    """
+    mock_mcp_references = [{"type": "mcp", "server_url": "http://localhost/mcp", "require_approval": "never"}]
+
+    mock_mcp_tools = [SimpleNamespace(name="mcp_tool_1", description="MCP Tool", inputSchema={"type": "object"})]
+    mock_tool_server_map = {"mcp_tool_1": "http://localhost/mcp"}
+
+    mock_anthropic_response = {
+        "id": "msg_123",
+        "type": "message",
+        "role": "assistant",
+        "content": [
+            {"type": "tool_use", "id": "call_mcp", "name": "mcp_tool_1", "input": {}},
+        ],
+        "stop_reason": "tool_use",
+    }
+
+    mock_context = MagicMock()
+    mock_context.user_api_key_auth = None
+    mock_context.litellm_trace_id = "trace_123"
+    mock_context.mcp_auth_header = None
+    mock_context.mcp_server_auth_headers = None
+    mock_context.request_tags = None
+    mock_context.oauth2_headers = None
+    mock_context.raw_headers = None
+    mock_context.litellm_call_id = "call_123"
+
+    path_resolve = "litellm.responses.mcp.request_context.MCPRequestContext.resolve"
+    path_parse = "litellm.responses.mcp.litellm_proxy_mcp_handler.LiteLLM_Proxy_MCP_Handler._parse_mcp_tools"
+    path_process = (
+        "litellm.responses.mcp.litellm_proxy_mcp_handler."
+        "LiteLLM_Proxy_MCP_Handler._process_mcp_tools_without_openai_transform"
+    )
+    path_auto = "litellm.responses.mcp.litellm_proxy_mcp_handler.LiteLLM_Proxy_MCP_Handler._should_auto_execute_tools"
+    path_exec = "litellm.responses.mcp.litellm_proxy_mcp_handler.LiteLLM_Proxy_MCP_Handler._execute_tool_calls"
+    path_call = "litellm.llms.anthropic.experimental_pass_through.messages.mcp_handler._AnthropicMessagesCall"
+
+    with (
+        patch(path_resolve, return_value=mock_context),
+        patch(path_parse, return_value=(mock_mcp_references, [])),
+        patch(path_process, new_callable=AsyncMock) as mock_process,
+        patch(path_auto, return_value=True),
+        patch(path_exec, new_callable=AsyncMock) as mock_execute,
+        patch(path_call) as mock_call,
+    ):
+        mock_process.return_value = (mock_mcp_tools, mock_tool_server_map)
+        mock_execute.return_value = [{"tool_call_id": "call_mcp", "result": "ok"}]
+
+        mock_final_response = {
+            "id": "msg_124",
+            "type": "message",
+            "role": "assistant",
+            "content": [{"type": "text", "text": "done"}],
+            "stop_reason": "end_turn",
+        }
+
+        mock_fn = AsyncMock(side_effect=[mock_anthropic_response, mock_final_response])
+        mock_call.return_value.fn = mock_fn
+
+        response = await anthropic_messages_with_mcp(
+            max_tokens=100,
+            messages=[{"role": "user", "content": "run mcp_tool_1"}],
+            model="claude-3-5-sonnet-20241022",
+            tools=mock_mcp_references,
+        )
+
+        mock_execute.assert_called_once()
+        assert response == mock_final_response