Standardize tool error capture with ToolErrorData across framework handlers

Signed-off-by: Eric Evans <194135482+ericevans-nv@users.noreply.github.com>

Author: ericevans-nv

docs/source/extend/custom-components/custom-evaluator.md

@@ -31,7 +31,7 @@ You can view the list of existing evaluators by running the following command:
 ```bash
 nat info components -t evaluator
 ```
-`ragas` is an example of an existing evaluator. The `ragas` evaluator is used to evaluate the accuracy of a workflow output.
+`ragas`, `trajectory`, `tunable_rag_evaluator`, and `tool_failure` are examples of existing evaluators. The `ragas` evaluator is used to evaluate the accuracy of a workflow output, while the `tool_failure` evaluator analyzes tool call success rates and error details.
 
 ## Extending NeMo Agent Toolkit with Custom Evaluators
 To extend NeMo Agent Toolkit with custom evaluators, you need to create an evaluator function and register it with NeMo Agent Toolkit by using the `register_evaluator` decorator.

docs/source/improve-workflows/evaluate.md

@@ -748,6 +748,7 @@ NeMo Agent Toolkit provides the following built-in evaluator:
 - `ragas` - An evaluator to run and evaluate workflows using the public Ragas API.
 - `trajectory` - An evaluator to run and evaluate the LangChain/LangGraph agent trajectory.
 - `tunable_rag_evaluator` - A customizable LLM evaluator for flexible RAG workflow evaluation.
+- `tool_failure` - An evaluator to analyze tool call success rates and surface structured error details.
 - `langsmith` - Built-in `openevals` evaluators (e.g., `exact_match`, `levenshtein_distance`).
 - `langsmith_custom` - Import any LangSmith-compatible evaluator by Python dotted path.
 - `langsmith_judge` - LLM-as-judge evaluator powered by `openevals`.
@@ -864,6 +865,63 @@ Note: In your evaluation dataset, make sure that the `answer` field is a descrip
 nat eval --config_file=examples/evaluation_and_profiling/simple_calculator_eval/configs/config-tunable-rag-eval.yml
 ```
 
+#### Tool Failure Evaluator
+The `tool_failure` evaluator analyzes tool call success rates and surfaces structured error details from workflow execution. This evaluator is useful for debugging tool reliability issues and understanding which tools are failing and why.
+
+The evaluator processes the workflow's intermediate steps to detect tool errors and calculates a success rate for each dataset entry. It provides detailed breakdowns of failed tool calls, including error messages, tool inputs, and outputs.
+
+**Example:**
+```yaml
+eval:
+  evaluators:
+    tool_failures:
+      _type: tool_failure
+      max_concurrency: 4
+```
+
+The evaluator produces the following output for each dataset entry:
+- **score**: Tool call success rate calculated as `(total_calls - failed_calls) / total_calls`. A score of 1.0 indicates all tool calls succeeded, while 0.0 indicates all failed.
+- **reasoning**: A structured breakdown containing:
+  - `total_tool_calls`: Total number of tool calls made during workflow execution
+  - `failed_tool_calls`: Number of tool calls that encountered errors
+  - `failed_tools`: List of tool names that failed (null if no failures)
+  - `per_tool_summary`: Detailed breakdown for each failing tool, including:
+    - Tool name
+    - Number of failed attempts
+    - List of failed calls with tool inputs, outputs, and error details
+
+**Sample Output:**
+```json
+{
+  "id": "1",
+  "score": 0.75,
+  "reasoning": {
+    "total_tool_calls": 4,
+    "failed_tool_calls": 1,
+    "failed_tools": ["database_query"],
+    "per_tool_summary": [
+      {
+        "tool_name": "database_query",
+        "failed_count": 1,
+        "attempts": [
+          {
+            "tool_input": "{\"query\": \"SELECT * FROM users\"}",
+            "tool_output": null,
+            "error": "DatabaseConnectionError: Connection timeout"
+          }
+        ]
+      }
+    ]
+  }
+}
+```
+
+The tool failure evaluator is particularly useful for:
+- Identifying unreliable tools in production workflows
+- Debugging tool integration issues during development
+- Monitoring tool error rates across different datasets
+- Understanding error patterns and failure modes
+
 #### LangSmith Evaluators
 
 NeMo Agent Toolkit integrates with [LangSmith](https://docs.smith.langchain.com/) and [OpenEvals](https://github.com/langchain-ai/openevals) to provide three evaluator types. To use these evaluators, install the LangChain integration package with one of the following commands, depending on whether you installed the NeMo Agent Toolkit from source or from a package.

packages/nvidia_nat_adk/src/nat/plugins/adk/callback_handler.py

@@ -25,6 +25,7 @@
 from nat.data_models.intermediate_step import IntermediateStepPayload
 from nat.data_models.intermediate_step import IntermediateStepType
 from nat.data_models.intermediate_step import StreamEventData
+from nat.data_models.intermediate_step import ToolErrorData
 from nat.data_models.intermediate_step import TraceMetadata
 from nat.data_models.intermediate_step import UsageInfo
 from nat.data_models.profiler_callback import BaseProfilerCallback
@@ -200,8 +201,29 @@ async def wrapped_tool_use(base_tool_instance, *args, **kwargs) -> Any:
 
                 return result
 
-            except Exception as _e:
-                logger.exception("BaseTool error occured")
+            except Exception as e:
+                logger.error("BaseTool error: %s", e)
+                kwargs_args = (kwargs.get("args", {}) if isinstance(kwargs.get("args"), dict) else {})
+                tool_error: ToolErrorData = ToolErrorData(
+                    content=f"{type(e).__name__}: {e!s}",
+                    error_type=type(e).__name__,
+                    error_message=str(e),
+                )
+                self.step_manager.push_intermediate_step(
+                    IntermediateStepPayload(
+                        event_type=IntermediateStepType.TOOL_END,
+                        span_event_timestamp=time.time(),
+                        framework=LLMFrameworkEnum.ADK,
+                        name=tool_name,
+                        data=StreamEventData(
+                            input={
+                                "args": args, "kwargs": dict(kwargs_args)
+                            },
+                            output=tool_error,
+                        ),
+                        usage_info=UsageInfo(token_usage=TokenUsageBaseModel()),
+                        UUID=step_uuid,
+                    ))
                 raise
 
         return wrapped_tool_use

packages/nvidia_nat_adk/tests/test_adk_callback_handler.py

@@ -23,6 +23,7 @@
 
 from nat.data_models.intermediate_step import IntermediateStepType
 from nat.data_models.intermediate_step import LLMFrameworkEnum
+from nat.data_models.intermediate_step import ToolErrorData
 from nat.data_models.profiler_callback import BaseProfilerCallback
 from nat.plugins.adk.callback_handler import ADKProfilerHandler
 
@@ -219,30 +220,34 @@ async def test_tool_use_monkey_patch_functionality(handler, mock_context):
 
 @pytest.mark.asyncio
 async def test_tool_use_monkey_patch_with_exception(handler, mock_context):
-    """Test tool use monkey patch handles exceptions properly."""
-    # Create a mock tool instance
+    """When a tool raises an exception, TOOL_END event contains ToolErrorData with parsed error details."""
     mock_tool_instance = MagicMock()
-    mock_tool_instance.name = "test_tool"
+    mock_tool_instance.name = "lookup_tool"
 
-    # Create mock original function that raises an exception
-    mock_original_func = AsyncMock(side_effect=Exception("Tool error"))
+    mock_original_func = AsyncMock(side_effect=ValueError("Column 'revenue' not found"))
     handler._original_tool_call = mock_original_func
 
-    # Get the wrapped function
     wrapped_func = handler._tool_use_monkey_patch()
 
-    # Test that exception is re-raised
-    with pytest.raises(Exception, match="Tool error"):
+    with pytest.raises(ValueError, match="Column 'revenue' not found"):
         await wrapped_func(mock_tool_instance, "arg1")
 
-    # Verify original function was called
     mock_original_func.assert_called_once()
 
-    # Verify start event was still pushed
-    assert mock_context.push_intermediate_step.call_count >= 1
+    assert mock_context.push_intermediate_step.call_count == 2
     start_call = mock_context.push_intermediate_step.call_args_list[0][0][0]
     assert start_call.event_type == IntermediateStepType.TOOL_START
 
+    end_call = mock_context.push_intermediate_step.call_args_list[1][0][0]
+    assert end_call.event_type == IntermediateStepType.TOOL_END
+    assert end_call.name == "lookup_tool"
+    assert isinstance(end_call.data.output, ToolErrorData)
+
+    error_data: ToolErrorData = end_call.data.output
+    assert error_data.content == "ValueError: Column 'revenue' not found"
+    assert error_data.error_type == "ValueError"
+    assert error_data.error_message == "Column 'revenue' not found"
+
 
 @pytest.mark.asyncio
 async def test_tool_use_monkey_patch_tool_name_error(handler, mock_context):

packages/nvidia_nat_autogen/src/nat/plugins/autogen/callback_handler.py

@@ -45,6 +45,7 @@
 from nat.data_models.intermediate_step import IntermediateStepPayload
 from nat.data_models.intermediate_step import IntermediateStepType
 from nat.data_models.intermediate_step import StreamEventData
+from nat.data_models.intermediate_step import ToolErrorData
 from nat.data_models.intermediate_step import TraceMetadata
 from nat.data_models.intermediate_step import UsageInfo
 from nat.data_models.profiler_callback import BaseProfilerCallback
@@ -557,15 +558,18 @@ async def wrapped_tool_call(*args: Any, **kwargs: Any) -> Any:
             except Exception:
                 logger.debug("Error getting tool name")
 
-            # Extract tool input
-            tool_input = ""
+            # Extract tool input from args
+            # run_json signature: (self, args: Mapping[str, Any], cancellation_token, call_id=None)
+            # args[0] = self (tool instance)
+            # args[1] = args (the tool arguments as a Mapping)
+            tool_input: dict[str, Any] = {}
             try:
                 if len(args) > 1:
-                    call_data = args[1]
-                    if hasattr(call_data, "kwargs"):
-                        tool_input = str(call_data.kwargs)
-                    elif isinstance(call_data, dict):
-                        tool_input = str(call_data.get("kwargs", {}))
+                    tool_args = args[1]
+                    if isinstance(tool_args, dict):
+                        tool_input = dict(tool_args)
+                    elif hasattr(tool_args, "items"):
+                        tool_input = dict(tool_args)
             except Exception:
                 logger.debug("Error extracting tool input")
 
@@ -590,14 +594,18 @@ async def wrapped_tool_call(*args: Any, **kwargs: Any) -> Any:
                 output = await original_func(*args, **kwargs)
             except Exception as e:
                 logger.error("Tool execution failed: %s", e)
+                tool_error: ToolErrorData = ToolErrorData(
+                    content=f"{type(e).__name__}: {e!s}",
+                    error_type=type(e).__name__,
+                    error_message=str(e),
+                )
                 handler.step_manager.push_intermediate_step(
                     IntermediateStepPayload(
                         event_type=IntermediateStepType.TOOL_END,
                         span_event_timestamp=time.time(),
                         framework=LLMFrameworkEnum.AUTOGEN,
                         name=tool_name,
-                        data=StreamEventData(input=tool_input, output=str(e)),
-                        metadata=TraceMetadata(error=str(e)),
+                        data=StreamEventData(input=tool_input, output=tool_error),
                         usage_info=UsageInfo(token_usage=TokenUsageBaseModel()),
                         UUID=start_uuid,
                     ))

packages/nvidia_nat_autogen/tests/test_callback_handler_autogen.py

@@ -22,6 +22,7 @@
 
 import pytest
 
+from nat.data_models.intermediate_step import ToolErrorData
 from nat.plugins.autogen.callback_handler import AutoGenProfilerHandler
 from nat.plugins.autogen.callback_handler import ClientPatchInfo
 from nat.plugins.autogen.callback_handler import PatchedClients
@@ -561,29 +562,56 @@ async def test_tool_wrapper_handles_dict_input(self, mock_get):
 
     @patch('nat.plugins.autogen.callback_handler.Context.get')
     async def test_tool_wrapper_handles_exception(self, mock_get):
-        """Test tool wrapper handles tool execution errors."""
+        """When a tool raises an exception, TOOL_END event contains ToolErrorData with parsed error details."""
         mock_context = Mock()
         mock_step_manager = Mock()
         mock_context.intermediate_step_manager = mock_step_manager
         mock_get.return_value = mock_context
 
         handler = AutoGenProfilerHandler()
 
-        original_func = AsyncMock(side_effect=ValueError("Tool failed"))
+        original_func = AsyncMock(side_effect=ValueError("Column 'revenue' not found"))
         wrapped = handler._create_tool_wrapper(original_func)
 
         tool = Mock()
-        tool.name = "failing_tool"
+        tool.name = "lookup_tool"
         call_data = Mock()
         call_data.kwargs = {}
 
-        with pytest.raises(ValueError, match="Tool failed"):
+        with pytest.raises(ValueError, match="Column 'revenue' not found"):
             await wrapped(tool, call_data)
 
-        # Should have START and error END
         assert mock_step_manager.push_intermediate_step.call_count == 2
         error_call = mock_step_manager.push_intermediate_step.call_args_list[1][0][0]
-        assert "Tool failed" in error_call.data.output
+        assert error_call.name == "lookup_tool"
+        assert isinstance(error_call.data.output, ToolErrorData)
+
+        error_data: ToolErrorData = error_call.data.output
+        assert error_data.content == "ValueError: Column 'revenue' not found"
+        assert error_data.error_type == "ValueError"
+        assert error_data.error_message == "Column 'revenue' not found"
+
+    @patch('nat.plugins.autogen.callback_handler.Context.get')
+    async def test_tool_wrapper_extracts_input_from_run_json_args(self, mock_get):
+        """Test tool wrapper extracts input from run_json signature: (self, args: Mapping, ...)."""
+        mock_context = Mock()
+        mock_step_manager = Mock()
+        mock_context.intermediate_step_manager = mock_step_manager
+        mock_get.return_value = mock_context
+
+        handler = AutoGenProfilerHandler()
+
+        original_func = AsyncMock(return_value="result")
+        wrapped = handler._create_tool_wrapper(original_func)
+
+        tool = Mock()
+        tool.name = "failing_lookup"
+        tool_args = {"query": "revenue"}
+
+        await wrapped(tool, tool_args, Mock())
+
+        start_event = mock_step_manager.push_intermediate_step.call_args_list[0][0][0]
+        assert start_event.data.input == {"query": "revenue"}
 
 
 class TestIntegration:

packages/nvidia_nat_core/src/nat/data_models/intermediate_step.py

@@ -68,6 +68,14 @@ class IntermediateStepState(StrEnum):
     END = "END"
 
 
+class ToolErrorData(BaseModel):
+    """ToolErrorData is a data model that represents the output field in a TOOL_END event when an error occurs."""
+
+    content: str = Field(description="Full error string, e.g. 'ValueError: Column not found'")
+    error_type: str = Field(description="Exception type, e.g. 'ValueError'")
+    error_message: str = Field(description="Error message without type, e.g. 'Column not found'")
+
+
 class StreamEventData(BaseModel):
     """
     StreamEventData is a data model that represents the data field in an streaming event.