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fix: ensure full type signature for tools #3973

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fix: ensure full type signature for tools #3973

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610c1bb
fix: ensure full type signature for tools
greysonlalonde Nov 25, 2025
d102ba8
Merge branch 'main' into gl/fix/ensure-complete-tool-signature
greysonlalonde Nov 25, 2025
70c90d8
Merge branch 'main' into gl/fix/ensure-complete-tool-signature
greysonlalonde Nov 26, 2025
120545a
Merge branch 'main' into gl/fix/ensure-complete-tool-signature
greysonlalonde Nov 27, 2025
273491f
Merge branch 'main' into gl/fix/ensure-complete-tool-signature
greysonlalonde Dec 1, 2025
c5f0abf
Merge branch 'main' into gl/fix/ensure-complete-tool-signature
greysonlalonde Dec 2, 2025
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169 changes: 87 additions & 82 deletions lib/crewai/src/crewai/tools/base_tool.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
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,
Expand Down Expand Up @@ -55,7 +55,7 @@ class _ArgsSchemaPlaceholder(PydanticBaseModel):
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.",
)
Expand All @@ -80,20 +80,21 @@ def _default_args_schema(
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
Expand Down Expand Up @@ -164,24 +165,21 @@ def from_langchain(cls, tool: Any) -> 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
)
Expand All @@ -195,20 +193,24 @@ def from_langchain(cls, tool: Any) -> BaseTool:

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:
Expand Down Expand Up @@ -241,13 +243,13 @@ def _get_arg_annotations(annotation: type[Any] | None) -> str:
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)
Expand Down Expand Up @@ -275,24 +277,21 @@ def from_langchain(cls, tool: Any) -> Tool:
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
)
Expand All @@ -312,37 +311,43 @@ 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.
result_as_answer: Flag to indicate if the tool result should be used as the final agent answer.
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,
Expand Down
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