fix: ensure full type signature for tools

lib/crewai/src/crewai/tools/base_tool.py

@@ -4,7 +4,7 @@ from abc import ABC, abstractmethod
 import asyncio
 from collections.abc import Callable
 from inspect import signature
-from typing import Any, cast, get_args, get_origin
+from typing import Any, get_args, get_origin
 
 from pydantic import (
     BaseModel,
@@ -55,7 +55,7 @@ class BaseTool(BaseModel, ABC):
         default=False, description="Flag to check if the description has been updated."
     )
 
-    cache_function: Callable = Field(
+    cache_function: Callable[..., bool] = Field(
         default=lambda _args=None, _result=None: True,
         description="Function that will be used to determine if the tool should be cached, should return a boolean. If None, the tool will be cached.",
     )
@@ -80,20 +80,21 @@ class BaseTool(BaseModel, ABC):
         if v != cls._ArgsSchemaPlaceholder:
             return v
 
-        return cast(
-            type[PydanticBaseModel],
-            type(
-                f"{cls.__name__}Schema",
-                (PydanticBaseModel,),
-                {
-                    "__annotations__": {
-                        k: v
-                        for k, v in cls._run.__annotations__.items()
-                        if k != "return"
-                    },
-                },
-            ),
-        )
+        run_sig = signature(cls._run)
+        fields: dict[str, Any] = {}
+
+        for param_name, param in run_sig.parameters.items():
+            if param_name in ("self", "return"):
+                continue
+
+            annotation = param.annotation if param.annotation != param.empty else Any
+
+            if param.default is param.empty:
+                fields[param_name] = (annotation, ...)
+            else:
+                fields[param_name] = (annotation, param.default)
+
+        return create_model(f"{cls.__name__}Schema", **fields)
 
     @field_validator("max_usage_count", mode="before")
     @classmethod
@@ -164,24 +165,21 @@ class BaseTool(BaseModel, ABC):
         args_schema = getattr(tool, "args_schema", None)
 
         if args_schema is None:
-            # Infer args_schema from the function signature if not provided
             func_signature = signature(tool.func)
-            annotations = func_signature.parameters
-            args_fields: dict[str, Any] = {}
-            for name, param in annotations.items():
-                if name != "self":
-                    param_annotation = (
-                        param.annotation if param.annotation != param.empty else Any
-                    )
-                    field_info = Field(
-                        default=...,
-                        description="",
-                    )
-                    args_fields[name] = (param_annotation, field_info)
-            if args_fields:
-                args_schema = create_model(f"{tool.name}Input", **args_fields)
+            fields: dict[str, Any] = {}
+            for name, param in func_signature.parameters.items():
+                if name == "self":
+                    continue
+                param_annotation = (
+                    param.annotation if param.annotation != param.empty else Any
+                )
+                if param.default is param.empty:
+                    fields[name] = (param_annotation, ...)
+                else:
+                    fields[name] = (param_annotation, param.default)
+            if fields:
+                args_schema = create_model(f"{tool.name}Input", **fields)
             else:
-                # Create a default schema with no fields if no parameters are found
                 args_schema = create_model(
                     f"{tool.name}Input", __base__=PydanticBaseModel
                 )
@@ -195,20 +193,24 @@ class BaseTool(BaseModel, ABC):
 
     def _set_args_schema(self) -> None:
         if self.args_schema is None:
-            class_name = f"{self.__class__.__name__}Schema"
-            self.args_schema = cast(
-                type[PydanticBaseModel],
-                type(
-                    class_name,
-                    (PydanticBaseModel,),
-                    {
-                        "__annotations__": {
-                            k: v
-                            for k, v in self._run.__annotations__.items()
-                            if k != "return"
-                        },
-                    },
-                ),
+            run_sig = signature(self._run)
+            fields: dict[str, Any] = {}
+
+            for param_name, param in run_sig.parameters.items():
+                if param_name in ("self", "return"):
+                    continue
+
+                annotation = (
+                    param.annotation if param.annotation != param.empty else Any
+                )
+
+                if param.default is param.empty:
+                    fields[param_name] = (annotation, ...)
+                else:
+                    fields[param_name] = (annotation, param.default)
+
+            self.args_schema = create_model(
+                f"{self.__class__.__name__}Schema", **fields
             )
 
     def _generate_description(self) -> None:
@@ -241,13 +243,13 @@ class BaseTool(BaseModel, ABC):
             args_str = ", ".join(BaseTool._get_arg_annotations(arg) for arg in args)
             return f"{origin.__name__}[{args_str}]"
 
-        return origin.__name__
+        return str(origin.__name__)
 
 
 class Tool(BaseTool):
     """The function that will be executed when the tool is called."""
 
-    func: Callable
+    func: Callable[..., Any]
 
     def _run(self, *args: Any, **kwargs: Any) -> Any:
         return self.func(*args, **kwargs)
@@ -275,24 +277,21 @@ class Tool(BaseTool):
         args_schema = getattr(tool, "args_schema", None)
 
         if args_schema is None:
-            # Infer args_schema from the function signature if not provided
             func_signature = signature(tool.func)
-            annotations = func_signature.parameters
-            args_fields: dict[str, Any] = {}
-            for name, param in annotations.items():
-                if name != "self":
-                    param_annotation = (
-                        param.annotation if param.annotation != param.empty else Any
-                    )
-                    field_info = Field(
-                        default=...,
-                        description="",
-                    )
-                    args_fields[name] = (param_annotation, field_info)
-            if args_fields:
-                args_schema = create_model(f"{tool.name}Input", **args_fields)
+            fields: dict[str, Any] = {}
+            for name, param in func_signature.parameters.items():
+                if name == "self":
+                    continue
+                param_annotation = (
+                    param.annotation if param.annotation != param.empty else Any
+                )
+                if param.default is param.empty:
+                    fields[name] = (param_annotation, ...)
+                else:
+                    fields[name] = (param_annotation, param.default)
+            if fields:
+                args_schema = create_model(f"{tool.name}Input", **fields)
             else:
-                # Create a default schema with no fields if no parameters are found
                 args_schema = create_model(
                     f"{tool.name}Input", __base__=PydanticBaseModel
                 )
@@ -312,10 +311,11 @@ def to_langchain(
 
 
 def tool(
-    *args, result_as_answer: bool = False, max_usage_count: int | None = None
-) -> Callable:
-    """
-    Decorator to create a tool from a function.
+    *args: Callable[..., Any] | str,
+    result_as_answer: bool = False,
+    max_usage_count: int | None = None,
+) -> Callable[..., Any] | BaseTool:
+    """Decorator to create a tool from a function.
 
     Args:
         *args: Positional arguments, either the function to decorate or the tool name.
@@ -323,26 +323,31 @@ def tool(
         max_usage_count: Maximum number of times this tool can be used. None means unlimited usage.
     """
 
-    def _make_with_name(tool_name: str) -> Callable:
-        def _make_tool(f: Callable) -> BaseTool:
+    def _make_with_name(tool_name: str) -> Callable[[Callable[..., Any]], BaseTool]:
+        def _make_tool(f: Callable[..., Any]) -> BaseTool:
             if f.__doc__ is None:
                 raise ValueError("Function must have a docstring")
             if f.__annotations__ is None:
                 raise ValueError("Function must have type annotations")
 
+            func_sig = signature(f)
+            fields: dict[str, Any] = {}
+
+            for param_name, param in func_sig.parameters.items():
+                if param_name == "return":
+                    continue
+
+                annotation = (
+                    param.annotation if param.annotation != param.empty else Any
+                )
+
+                if param.default is param.empty:
+                    fields[param_name] = (annotation, ...)
+                else:
+                    fields[param_name] = (annotation, param.default)
+
             class_name = "".join(tool_name.split()).title()
-            args_schema = cast(
-                type[PydanticBaseModel],
-                type(
-                    class_name,
-                    (PydanticBaseModel,),
-                    {
-                        "__annotations__": {
-                            k: v for k, v in f.__annotations__.items() if k != "return"
-                        },
-                    },
-                ),
-            )
+            args_schema = create_model(class_name, **fields)
 
             return Tool(
                 name=tool_name,

lib/crewai/tests/tools/test_base_tool.py

@@ -230,3 +230,67 @@ def test_max_usage_count_is_respected():
     crew.kickoff()
     assert tool.max_usage_count == 5
     assert tool.current_usage_count == 5
+
+
+def test_tool_schema_respects_default_values():
+    """Test that tool schema correctly marks optional parameters with defaults."""
+
+    class ToolWithDefaults(BaseTool):
+        name: str = "tool_with_defaults"
+        description: str = "A tool with optional parameters"
+
+        def _run(
+            self,
+            query: str,
+            similarity_threshold: float | None = None,
+            limit: int = 5,
+        ) -> str:
+            return f"{query} - {similarity_threshold} - {limit}"
+
+    tool = ToolWithDefaults()
+    schema = tool.args_schema.model_json_schema()
+
+    assert schema["required"] == ["query"]
+
+    props = schema["properties"]
+    assert "default" in props["similarity_threshold"]
+    assert props["similarity_threshold"]["default"] is None
+    assert "default" in props["limit"]
+    assert props["limit"]["default"] == 5
+
+
+def test_tool_decorator_respects_default_values():
+    """Test that @tool decorator correctly handles optional parameters with defaults."""
+
+    @tool("search_tool")
+    def search_with_defaults(
+        query: str, max_results: int = 10, sort_by: str | None = None
+    ) -> str:
+        """Search for information with optional parameters."""
+        return f"{query} - {max_results} - {sort_by}"
+
+    schema = search_with_defaults.args_schema.model_json_schema()
+
+    assert schema["required"] == ["query"]
+
+    props = schema["properties"]
+    assert "default" in props["max_results"]
+    assert props["max_results"]["default"] == 10
+    assert "default" in props["sort_by"]
+    assert props["sort_by"]["default"] is None
+
+
+def test_tool_schema_all_required_when_no_defaults():
+    """Test that all parameters are required when no defaults are provided."""
+
+    class AllRequiredTool(BaseTool):
+        name: str = "all_required"
+        description: str = "All params required"
+
+        def _run(self, param1: str, param2: int, param3: bool) -> str:
+            return f"{param1} - {param2} - {param3}"
+
+    tool = AllRequiredTool()
+    schema = tool.args_schema.model_json_schema()
+
+    assert set(schema["required"]) == {"param1", "param2", "param3"}