feat: improve ToolMetric in Agent Evaluator when the agent fails to invoke a required tool

src/crewai/experimental/evaluation/metrics/tools_metrics.py

@@ -24,33 +24,29 @@ class ToolSelectionEvaluator(BaseEvaluator):
         if task is not None:
             task_context = f"Task description: {task.description}"
 
+        if not agent.tools:
+            return EvaluationScore(
+                score=None,
+                feedback="Agent had no tools available to use."
+            )
+
         tool_uses = execution_trace.get("tool_uses", [])
         tool_count = len(tool_uses)
         unique_tool_types = set([tool.get("tool", "Unknown tool") for tool in tool_uses])
+        available_tools_info = "No tools available"
 
-        if tool_count == 0:
-            if not agent.tools:
-                return EvaluationScore(
-                    score=None,
-                    feedback="Agent had no tools available to use."
-                )
-            else:
-                return EvaluationScore(
-                    score=None,
-                    feedback="Agent had tools available but didn't use any."
-                )
-
-        available_tools_info = ""
         if agent.tools:
+            available_tools_info = ""
             for tool in agent.tools:
                 available_tools_info += f"- {tool.name}: {tool.description}\n"
-        else:
-            available_tools_info = "No tools available"
 
         tool_types_summary = "Tools selected by the agent:\n"
         for tool_type in sorted(unique_tool_types):
             tool_types_summary += f"- {tool_type}\n"
 
+        if tool_count == 0:
+            tool_types_summary += "No tools used"
+
         prompt = [
             {"role": "system", "content": """You are an expert evaluator assessing if an AI agent selected the most appropriate tools for a given task.
 
@@ -92,6 +88,7 @@ IMPORTANT:
 """}
         ]
         assert self.llm is not None
+        breakpoint()
         response = self.llm.call(prompt)
 
         try:

tests/experimental/evaluation/metrics/test_tools_metrics.py

@@ -1,3 +1,4 @@
+import pytest
 from unittest.mock import patch, MagicMock
 
 from crewai.experimental.evaluation.metrics.tools_metrics import (
@@ -7,6 +8,26 @@ from crewai.experimental.evaluation.metrics.tools_metrics import (
 )
 from crewai.utilities.llm_utils import LLM
 from tests.experimental.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
+from crewai.tools.base_tool import BaseTool
+
+@pytest.fixture
+def tools_available():
+    class MyCustomTool(BaseTool):
+        name: str = "Name of my tool"
+        description: str = "Clear description for what this tool is useful for, your agent will need this information to use it."
+
+        def _run(self, question: str) -> str:
+            return question
+
+    class AnotherCustomTool(BaseTool):
+        name: str = "Another name of my tool"
+        description: str = "Clear description for what this another tool is useful for, your agent will need this information to use it."
+
+        def _run(self, question: str) -> str:
+            return question
+
+    return [MyCustomTool(), AnotherCustomTool()]
+
 
 class TestToolSelectionEvaluator(BaseEvaluationMetricsTest):
     def test_no_tools_available(self, mock_task, mock_agent):
@@ -26,8 +47,8 @@ class TestToolSelectionEvaluator(BaseEvaluationMetricsTest):
         assert result.score is None
         assert "no tools available" in result.feedback.lower()
 
-    def test_tools_available_but_none_used(self, mock_agent, mock_task):
+    def test_tools_available_but_none_used(self, mock_agent, mock_task, tools_available):
-        mock_agent.tools = ["tool1", "tool2"]
+        mock_agent.tools = tools_available
         execution_trace = {"tool_uses": []}
 
         evaluator = ToolSelectionEvaluator()
@@ -42,7 +63,8 @@ class TestToolSelectionEvaluator(BaseEvaluationMetricsTest):
         assert "had tools available but didn't use any" in result.feedback.lower()
 
     @patch("crewai.utilities.llm_utils.create_llm")
-    def test_successful_evaluation(self, mock_create_llm, mock_agent, mock_task):
+    def test_successful_evaluation(self, mock_create_llm, mock_agent, mock_task, tools_available):
+        mock_agent.tools = tools_available
         # Setup mock LLM response
         mock_llm = MagicMock(spec=LLM)
         mock_llm.call.return_value = """