import json from typing import List, Optional import pytest from pydantic import BaseModel, Field from unittest import mock from generate_tool_specs import ToolSpecExtractor from crewai.tools.base_tool import EnvVar class MockToolSchema(BaseModel): query: str = Field(..., description="The query parameter") count: int = Field(5, description="Number of results to return") filters: Optional[List[str]] = Field(None, description="Optional filters to apply") class MockTool: name = "Mock Search Tool" description = "A tool that mocks search functionality" args_schema = MockToolSchema @pytest.fixture def extractor(): ext = ToolSpecExtractor() MockTool.__pydantic_core_schema__ = create_mock_schema(MockTool) MockTool.args_schema.__pydantic_core_schema__ = create_mock_schema_args(MockTool.args_schema) return ext def create_mock_schema(cls): return { "type": "model", "cls": cls, "schema": { "type": "model-fields", "fields": { "name": {"type": "model-field", "schema": {"type": "default", "schema": {"type": "str"}, "default": cls.name}, "metadata": {}}, "description": {"type": "model-field", "schema": {"type": "default", "schema": {"type": "str"}, "default": cls.description}, "metadata": {}}, "args_schema": {"type": "model-field", "schema": {"type": "default", "schema": {"type": "is-subclass", "cls": BaseModel}, "default": cls.args_schema}, "metadata": {}}, "env_vars": { "type": "model-field", "schema": {"type": "default", "schema": {"type": "list", "items_schema": {"type": "model", "cls": "INSPECT CLASS", "schema": {"type": "model-fields", "fields": {"name": {"type": "model-field", "schema": {"type": "str"}, "metadata": {}}, "description": {"type": "model-field", "schema": {"type": "str"}, "metadata": {}}, "required": {"type": "model-field", "schema": {"type": "default", "schema": {"type": "bool"}, "default": True}, "metadata": {}}, "default": {"type": "model-field", "schema": {"type": "default", "schema": {"type": "nullable", "schema": {"type": "str"}}, "default": None}, "metadata": {}},}, "model_name": "EnvVar", "computed_fields": []}, "custom_init": False, "root_model": False, "config": {"title": "EnvVar"}, "ref": "crewai.tools.base_tool.EnvVar:4593650640", "metadata": {"pydantic_js_functions": ["INSPECT __get_pydantic_json_schema__"]}}}, "default": [EnvVar(name='SERPER_API_KEY', description='API key for Serper', required=True, default=None), EnvVar(name='API_RATE_LIMIT', description='API rate limit', required=False, default="100")]}, "metadata": {} } }, "model_name": cls.__name__ } } def create_mock_schema_args(cls): return { "type": "model", "cls": cls, "schema": { "type": "model-fields", "fields": { "query": {"type": "model-field", "schema": {"type": "default", "schema": {"type": "str"}, "default": "The query parameter"}}, "count": {"type": "model-field", "schema": {"type": "default", "schema": {"type": "int"}, "default": 5}, "metadata": {"pydantic_js_updates": {"description": "Number of results to return"}}}, "filters": {"type": "model-field", "schema": {"type": "nullable", "schema": {"type": "list", "items_schema": {"type": "str"}}}} }, "model_name": cls.__name__ } } def test_unwrap_schema(extractor): nested_schema = { "type": "function-after", "schema": {"type": "default", "schema": {"type": "str", "value": "test"}} } result = extractor._unwrap_schema(nested_schema) assert result["type"] == "str" assert result["value"] == "test" @pytest.mark.parametrize( "field, fallback, expected", [ ({"schema": {"default": "test_value"}}, None, "test_value"), ({}, "fallback_value", "fallback_value"), ({"schema": {"default": 123}}, "fallback_value", "fallback_value") ] ) def test_extract_field_default(extractor, field, fallback, expected): result = extractor._extract_field_default(field, fallback=fallback) assert result == expected @pytest.mark.parametrize( "schema, expected", [ ({"type": "str"}, "str"), ({"type": "list", "items_schema": {"type": "str"}}, "list[str]"), ({"type": "dict", "keys_schema": {"type": "str"}, "values_schema": {"type": "int"}}, "dict[str, int]"), ({"type": "union", "choices": [{"type": "str"}, {"type": "int"}]}, "union[str, int]"), ({"type": "custom_type"}, "custom_type"), ({}, "unknown"), ] ) def test_schema_type_to_str(extractor, schema, expected): assert extractor._schema_type_to_str(schema) == expected @pytest.mark.parametrize( "info, expected_type", [ ({"schema": {"type": "str"}}, "str"), ({"schema": {"type": "nullable", "schema": {"type": "int"}}}, "int"), ({"schema": {"type": "default", "schema": {"type": "list", "items_schema": {"type": "str"}}}}, "list[str]"), ] ) def test_extract_param_type(extractor, info, expected_type): assert extractor._extract_param_type(info) == expected_type def test_extract_tool_info(extractor): with mock.patch("generate_tool_specs.dir", return_value=["MockTool"]), \ mock.patch("generate_tool_specs.getattr", return_value=MockTool): extractor.extract_all_tools() assert len(extractor.tools_spec) == 1 tool_info = extractor.tools_spec[0] assert tool_info["name"] == "MockTool" assert tool_info["humanized_name"] == "Mock Search Tool" assert tool_info["description"] == "A tool that mocks search functionality" assert len(tool_info["env_vars"]) == 2 api_key_var, rate_limit_var = tool_info["env_vars"] assert api_key_var["name"] == "SERPER_API_KEY" assert api_key_var["description"] == "API key for Serper" assert api_key_var["required"] == True assert api_key_var["default"] == None assert rate_limit_var["name"] == "API_RATE_LIMIT" assert rate_limit_var["description"] == "API rate limit" assert rate_limit_var["required"] == False assert rate_limit_var["default"] == "100" assert len(tool_info["run_params"]) == 3 params = {p["name"]: p for p in tool_info["run_params"]} assert params["query"]["description"] == "The query parameter" assert params["query"]["type"] == "str" assert params["count"]["description"] == "Number of results to return" assert params["count"]["type"] == "int" assert params["filters"]["description"] == "" assert params["filters"]["type"] == "list[str]" def test_save_to_json(extractor, tmp_path): extractor.tools_spec = [{ "name": "TestTool", "humanized_name": "Test Tool", "description": "A test tool", "run_params": [ {"name": "param1", "description": "Test parameter", "type": "str"} ] }] file_path = tmp_path / "output.json" extractor.save_to_json(str(file_path)) assert file_path.exists() with open(file_path, "r") as f: data = json.load(f) assert "tools" in data assert len(data["tools"]) == 1 assert data["tools"][0]["humanized_name"] == "Test Tool" assert data["tools"][0]["run_params"][0]["name"] == "param1" @pytest.mark.integration def test_full_extraction_process(): extractor = ToolSpecExtractor() specs = extractor.extract_all_tools() assert len(specs) > 0 for tool in specs: assert "name" in tool assert "humanized_name" in tool and tool["humanized_name"] assert "description" in tool assert isinstance(tool["run_params"], list) for param in tool["run_params"]: assert "name" in param and param["name"]