fix: Update base_agent_tools and base_tool to fix type errors

Co-Authored-By: Joe Moura <joao@crewai.com>

src/crewai/tools/base_tool.py

@@ -10,4 +10,160 @@ def _create_model_fields(fields: Dict[str, Tuple[Any, FieldInfo]]) -> Dict[str,
     """Helper function to create model fields with proper type hints."""
     return {name: (annotation, field) for name, (annotation, field) in fields.items()}
 
-# Rest of base_tool.py content...
+class BaseTool(BaseModel, ABC):
+    """Base class for all tools."""
+
+    class _ArgsSchemaPlaceholder(PydanticBaseModel):
+        pass
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    func: Optional[Callable] = None
+
+    name: str
+    """The unique name of the tool that clearly communicates its purpose."""
+    description: str
+    """Used to tell the model how/when/why to use the tool."""
+    args_schema: Type[PydanticBaseModel] = Field(default_factory=_ArgsSchemaPlaceholder)
+    """The schema for the arguments that the tool accepts."""
+    description_updated: bool = False
+    """Flag to check if the description has been updated."""
+    cache_function: Callable = lambda _args=None, _result=None: True
+    """Function that will be used to determine if the tool should be cached."""
+    result_as_answer: bool = False
+    """Flag to check if the tool should be the final agent answer."""
+
+    @validator("args_schema", always=True, pre=True)
+    def _default_args_schema(
+        cls, v: Type[PydanticBaseModel]
+    ) -> Type[PydanticBaseModel]:
+        if not isinstance(v, cls._ArgsSchemaPlaceholder):
+            return v
+
+        return type(
+            f"{cls.__name__}Schema",
+            (PydanticBaseModel,),
+            {
+                "__annotations__": {
+                    k: v for k, v in cls._run.__annotations__.items() if k != "return"
+                },
+            },
+        )
+
+    def model_post_init(self, __context: Any) -> None:
+        self._generate_description()
+        super().model_post_init(__context)
+
+    def run(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> Any:
+        print(f"Using Tool: {self.name}")
+        return self._run(*args, **kwargs)
+
+    @abstractmethod
+    def _run(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> Any:
+        """Here goes the actual implementation of the tool."""
+
+    def _set_args_schema(self) -> None:
+        if self.args_schema is None:
+            class_name = f"{self.__class__.__name__}Schema"
+            self.args_schema = type(
+                class_name,
+                (PydanticBaseModel,),
+                {
+                    "__annotations__": {
+                        k: v
+                        for k, v in self._run.__annotations__.items()
+                        if k != "return"
+                    },
+                },
+            )
+
+    def _generate_description(self) -> None:
+        args_schema = {
+            name: {
+                "description": field.description,
+                "type": BaseTool._get_arg_annotations(field.annotation),
+            }
+            for name, field in self.args_schema.model_fields.items()
+        }
+
+        self.description = f"Tool Name: {self.name}\nTool Arguments: {args_schema}\nTool Description: {self.description}"
+
+    @staticmethod
+    def _get_arg_annotations(annotation: type[Any] | None) -> str:
+        if annotation is None:
+            return "None"
+
+        origin = get_origin(annotation)
+        args = get_args(annotation)
+
+        if origin is None:
+            return (
+                annotation.__name__
+                if hasattr(annotation, "__name__")
+                else str(annotation)
+            )
+
+        if args:
+            args_str = ", ".join(BaseTool._get_arg_annotations(arg) for arg in args)
+            return f"{origin.__name__}[{args_str}]"
+
+        return origin.__name__
+
+
+class Tool(BaseTool):
+    """Tool class that wraps a function."""
+
+    func: Callable
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    def __init__(self, **kwargs):
+        if "func" not in kwargs:
+            raise ValueError("Tool requires a 'func' argument")
+        super().__init__(**kwargs)
+
+    def _run(self, *args: Any, **kwargs: Any) -> Any:
+        return self.func(*args, **kwargs)
+
+
+def tool(*args: Any) -> Any:
+    """Decorator to create a tool from a function."""
+
+    def _make_with_name(tool_name: str) -> Callable:
+        def _make_tool(f: Callable) -> Tool:
+            if f.__doc__ is None:
+                raise ValueError("Function must have a docstring")
+            if f.__annotations__ is None:
+                raise ValueError("Function must have type annotations")
+
+            class_name = "".join(tool_name.split()).title()
+            args_schema = type(
+                class_name,
+                (PydanticBaseModel,),
+                {
+                    "__annotations__": {
+                        k: v for k, v in f.__annotations__.items() if k != "return"
+                    },
+                },
+            )
+
+            return Tool(
+                name=tool_name,
+                description=f.__doc__,
+                func=f,
+                args_schema=args_schema,
+            )
+
+        return _make_tool
+
+    if len(args) == 1 and callable(args[0]):
+        return _make_with_name(args[0].__name__)(args[0])
+    if len(args) == 1 and isinstance(args[0], str):
+        return _make_with_name(args[0])
+    raise ValueError("Invalid arguments")