feat: use json schema for tool argument serialization

- Replace Python representation with JsonSchema for tool arguments
  - Remove deprecated PydanticSchemaParser in favor of direct schema generation
  - Add handling for VAR_POSITIONAL and VAR_KEYWORD parameters
  - Improve tool argument schema collection

lib/crewai/tests/tools/test_base_tool.py

@@ -17,10 +17,11 @@ def test_creating_a_tool_using_annotation():
 
     # Assert all the right attributes were defined
     assert my_tool.name == "Name of my tool"
-    assert (
-        my_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
-    )
+    assert "Tool Name: Name of my tool" in my_tool.description
+    assert "Tool Arguments:" in my_tool.description
+    assert '"question"' in my_tool.description
+    assert '"type": "string"' in my_tool.description
+    assert "Tool Description: Clear description for what this tool is useful for" in my_tool.description
     assert my_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }
@@ -31,10 +32,9 @@ def test_creating_a_tool_using_annotation():
     converted_tool = my_tool.to_structured_tool()
     assert converted_tool.name == "Name of my tool"
 
-    assert (
-        converted_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
-    )
+    assert "Tool Name: Name of my tool" in converted_tool.description
+    assert "Tool Arguments:" in converted_tool.description
+    assert '"question"' in converted_tool.description
     assert converted_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }
@@ -56,10 +56,11 @@ def test_creating_a_tool_using_baseclass():
     # Assert all the right attributes were defined
     assert my_tool.name == "Name of my tool"
 
-    assert (
-        my_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
-    )
+    assert "Tool Name: Name of my tool" in my_tool.description
+    assert "Tool Arguments:" in my_tool.description
+    assert '"question"' in my_tool.description
+    assert '"type": "string"' in my_tool.description
+    assert "Tool Description: Clear description for what this tool is useful for" in my_tool.description
     assert my_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }
@@ -68,10 +69,9 @@ def test_creating_a_tool_using_baseclass():
     converted_tool = my_tool.to_structured_tool()
     assert converted_tool.name == "Name of my tool"
 
-    assert (
-        converted_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
-    )
+    assert "Tool Name: Name of my tool" in converted_tool.description
+    assert "Tool Arguments:" in converted_tool.description
+    assert '"question"' in converted_tool.description
     assert converted_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }

lib/crewai/tests/tools/test_tool_usage.py

@@ -107,25 +107,20 @@ def test_tool_usage_render():
 
     rendered = tool_usage._render()
 
-    # Updated checks to match the actual output
+    # Check that the rendered output contains the expected tool information
     assert "Tool Name: Random Number Generator" in rendered
     assert "Tool Arguments:" in rendered
-    assert (
-        "'min_value': {'description': 'The minimum value of the range (inclusive)', 'type': 'int'}"
-        in rendered
-    )
-    assert (
-        "'max_value': {'description': 'The maximum value of the range (inclusive)', 'type': 'int'}"
-        in rendered
-    )
     assert (
         "Tool Description: Generates a random number within a specified range"
         in rendered
     )
-    assert (
-        "Tool Name: Random Number Generator\nTool Arguments: {'min_value': {'description': 'The minimum value of the range (inclusive)', 'type': 'int'}, 'max_value': {'description': 'The maximum value of the range (inclusive)', 'type': 'int'}}\nTool Description: Generates a random number within a specified range"
-        in rendered
-    )
+
+    # Check that the JSON schema format is used (proper JSON schema types)
+    assert '"min_value"' in rendered
+    assert '"max_value"' in rendered
+    assert '"type": "integer"' in rendered
+    assert '"description": "The minimum value of the range (inclusive)"' in rendered
+    assert '"description": "The maximum value of the range (inclusive)"' in rendered
 
 
 def test_validate_tool_input_booleans_and_none():

lib/crewai/tests/utilities/evaluators/test_task_evaluator.py

@@ -1,4 +1,3 @@
-from unittest import mock
 from unittest.mock import MagicMock, patch
 
 from crewai.utilities.converter import ConverterError
@@ -44,26 +43,26 @@ def test_evaluate_training_data(converter_mock):
     )
 
     assert result == function_return_value
-    converter_mock.assert_has_calls(
-        [
-            mock.call(
-                llm=original_agent.llm,
-                text="Assess the quality of the training data based on the llm output, human feedback , and llm "
-                "output improved result.\n\nIteration: data1\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback "
-                "1\n\nImproved Output:\nImproved output 1\n\n------------------------------------------------\n\nIteration: data2\nInitial Output:\nInitial output 2\n\nHuman "
-                "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\n------------------------------------------------\n\nPlease provide:\n- Provide "
-                "a list of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent's "
-                "performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific "
-                "action items for future tasks. Ensure all key and specificpoints from the human feedback are "
-                "incorporated into these instructions.\n- A score from 0 to 10 evaluating on completion, quality, and "
-                "overall performance from the improved output to the initial output based on the human feedback\n",
-                model=TrainingTaskEvaluation,
-                instructions="I'm gonna convert this raw text into valid JSON.\n\nThe json should have the "
-                "following structure, with the following keys:\n{\n    suggestions: List[str],\n    quality: float,\n    final_summary: str\n}",
-            ),
-            mock.call().to_pydantic(),
-        ]
-    )
+
+    # Verify the converter was called with correct arguments
+    converter_mock.assert_called_once()
+    call_kwargs = converter_mock.call_args.kwargs
+
+    assert call_kwargs["llm"] == original_agent.llm
+    assert call_kwargs["model"] == TrainingTaskEvaluation
+    assert "Iteration: data1" in call_kwargs["text"]
+    assert "Iteration: data2" in call_kwargs["text"]
+
+    instructions = call_kwargs["instructions"]
+    assert "I'm gonna convert this raw text into valid JSON." in instructions
+    assert "OpenAPI schema" in instructions
+    assert '"type": "json_schema"' in instructions
+    assert '"name": "TrainingTaskEvaluation"' in instructions
+    assert '"suggestions"' in instructions
+    assert '"quality"' in instructions
+    assert '"final_summary"' in instructions
+
+    converter_mock.return_value.to_pydantic.assert_called_once()
 
 
 @patch("crewai.utilities.converter.Converter.to_pydantic")

lib/crewai/tests/utilities/test_converter.py

@@ -16,7 +16,6 @@ from crewai.utilities.converter import (
     handle_partial_json,
     validate_model,
 )
-from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 from pydantic import BaseModel
 import pytest
 

lib/crewai/tests/utilities/test_pydantic_schema_parser.py

@@ -1,94 +0,0 @@
-from typing import Any, Dict, List, Optional, Set, Tuple, Union
-
-import pytest
-from pydantic import BaseModel, Field
-
-from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
-
-
-def test_simple_model():
-    class SimpleModel(BaseModel):
-        field1: int
-        field2: str
-
-    parser = PydanticSchemaParser(model=SimpleModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    field1: int,
-    field2: str
-}"""
-    assert schema.strip() == expected_schema.strip()
-
-
-def test_nested_model():
-    class NestedModel(BaseModel):
-        nested_field: int
-
-    class ParentModel(BaseModel):
-        parent_field: str
-        nested: NestedModel
-
-    parser = PydanticSchemaParser(model=ParentModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    parent_field: str,
-    nested: NestedModel
-    {
-        nested_field: int
-    }
-}"""
-    assert schema.strip() == expected_schema.strip()
-
-
-def test_model_with_list():
-    class ListModel(BaseModel):
-        list_field: List[int]
-
-    parser = PydanticSchemaParser(model=ListModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    list_field: List[int]
-}"""
-    assert schema.strip() == expected_schema.strip()
-
-
-def test_model_with_optional_field():
-    class OptionalModel(BaseModel):
-        optional_field: Optional[str]
-
-    parser = PydanticSchemaParser(model=OptionalModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    optional_field: Optional[str]
-}"""
-    assert schema.strip() == expected_schema.strip()
-
-
-def test_model_with_union():
-    class UnionModel(BaseModel):
-        union_field: Union[int, str]
-
-    parser = PydanticSchemaParser(model=UnionModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    union_field: Union[int, str]
-}"""
-    assert schema.strip() == expected_schema.strip()
-
-
-def test_model_with_dict():
-    class DictModel(BaseModel):
-        dict_field: Dict[str, int]
-
-    parser = PydanticSchemaParser(model=DictModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    dict_field: Dict[str, int]
-}"""
-    assert schema.strip() == expected_schema.strip()