lib/crewai-tools/tests/base_tool_test.py - main - githubmirror/crewai

lib/crewai-tools/tests/base_tool_test.py
100 строк4 KB
Greyson LaLonde
chore(crewai-tools): drop self-explanatory comments
27 май 2026, 02:25
27 май 2026, 02:25c5ea415
from collections.abc import Callable

from crewai.tools import BaseTool, tool
from crewai.tools.base_tool import to_langchain


def test_creating_a_tool_using_annotation():
    @tool("Name of my tool")
    def my_tool(question: str) -> str:
        """Clear description for what this tool is useful for, you agent will need this information to use it."""
        return question

    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, you agent will need this information to use it."
    )
    assert my_tool.args_schema.model_json_schema()["properties"] == {
        "question": {"title": "Question", "type": "string"}
    }
    assert (
        my_tool.func("What is the meaning of life?") == "What is the meaning of life?"
    )

    # Assert the langchain tool conversion worked as expected
    converted_tool = to_langchain([my_tool])[0]
    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, you agent will need this information to use it."
    )
    assert converted_tool.args_schema.model_json_schema()["properties"] == {
        "question": {"title": "Question", "type": "string"}
    }
    assert (
        converted_tool.func("What is the meaning of life?")
        == "What is the meaning of life?"
    )


def test_creating_a_tool_using_baseclass():
    class MyCustomTool(BaseTool):
        name: str = "Name of my tool"
        description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."

        def _run(self, question: str) -> str:
            return question

    my_tool = MyCustomTool()
    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, you agent will need this information to use it."
    )
    assert my_tool.args_schema.model_json_schema()["properties"] == {
        "question": {"title": "Question", "type": "string"}
    }
    assert (
        my_tool._run("What is the meaning of life?") == "What is the meaning of life?"
    )

    # Assert the langchain tool conversion worked as expected
    converted_tool = to_langchain([my_tool])[0]
    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, you agent will need this information to use it."
    )
    assert converted_tool.args_schema.model_json_schema()["properties"] == {
        "question": {"title": "Question", "type": "string"}
    }
    assert (
        converted_tool.invoke({"question": "What is the meaning of life?"})
        == "What is the meaning of life?"
    )


def test_setting_cache_function():
    class MyCustomTool(BaseTool):
        name: str = "Name of my tool"
        description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
        cache_function: Callable = lambda: False

        def _run(self, question: str) -> str:
            return question

    my_tool = MyCustomTool()
    assert not my_tool.cache_function()


def test_default_cache_function_is_true():
    class MyCustomTool(BaseTool):
        name: str = "Name of my tool"
        description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."

        def _run(self, question: str) -> str:
            return question

    my_tool = MyCustomTool()
    assert my_tool.cache_function()