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fix: Support list[BaseModel] annotation about FunctionTool args #3186

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31 changes: 30 additions & 1 deletion src/google/adk/tools/function_tool.py
View file Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -102,6 +102,7 @@ def _preprocess_args(self, args: dict[str, Any]) -> dict[str, Any]:

Currently handles:
- Converting JSON dictionaries to Pydantic model instances where expected
- Converting lists of JSON dictionaries to lists of Pydantic model instances

Future extensions could include:
- Type coercion for other complex types
Expand Down Expand Up @@ -129,8 +130,36 @@ def _preprocess_args(self, args: dict[str, Any]) -> dict[str, Any]:
if len(non_none_types) == 1:
target_type = non_none_types[0]

# Check if the target type is a list
if get_origin(target_type) is list:
list_args = get_args(target_type)
if list_args:
element_type = list_args[0]

# Check if the element type is a Pydantic model
if inspect.isclass(element_type) and issubclass(
element_type, pydantic.BaseModel
):
# Skip conversion if the value is None
if args[param_name] is None:
continue

# Convert list elements to Pydantic models
if isinstance(args[param_name], list):
converted_list = []
for item in args[param_name]:
try:
converted_list.append(element_type.model_validate(item))
except Exception as e:
# Skip items that fail validation
logger.warning(
f"Skipping item in '{param_name}': "
f'Failed to convert to {element_type.__name__}: {e}'
)
Comment on lines +153 to +158
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@gemini-code-assist gemini-code-assist bot Oct 15, 2025

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medium

While using except Exception is robust, it's generally better practice to catch more specific exceptions. This prevents accidentally catching and silencing unrelated errors (like KeyboardInterrupt or SystemExit) and makes the code's intent clearer. In this case, pydantic.ValidationError is the most expected exception during model validation.

Suggested change
except Exception as e:
# Skip items that fail validation
logger.warning(
f"Skipping item in '{param_name}': "
f'Failed to convert to {element_type.__name__}: {e}'
)
except pydantic.ValidationError as e:
# Skip items that fail validation
logger.warning(
f"Skipping item in '{param_name}': "
f'Failed to convert to {element_type.__name__}: {e}'
)

converted_args[param_name] = converted_list

# Check if the target type is a Pydantic model
if inspect.isclass(target_type) and issubclass(
elif inspect.isclass(target_type) and issubclass(
target_type, pydantic.BaseModel
):
# Skip conversion if the value is None and the parameter is Optional
Expand Down
120 changes: 118 additions & 2 deletions tests/unittests/tools/test_function_tool_pydantic.py
View file Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -280,5 +280,121 @@ async def test_run_async_with_optional_pydantic_models():
assert result["theme"] == "dark"
assert result["notifications"] is True
assert result["preferences_type"] == "PreferencesModel"
assert result["preferences_type"] == "PreferencesModel"
assert result["preferences_type"] == "PreferencesModel"


def function_with_list_of_pydantic_models(users: list[UserModel]) -> dict:
"""Function that takes a list of Pydantic models."""
return {
"count": len(users),
"names": [user.name for user in users],
"ages": [user.age for user in users],
"types": [type(user).__name__ for user in users],
}


def function_with_optional_list_of_pydantic_models(
users: Optional[list[UserModel]] = None,
) -> dict:
"""Function that takes an optional list of Pydantic models."""
if users is None:
return {"count": 0, "names": []}
return {
"count": len(users),
"names": [user.name for user in users],
}


def test_preprocess_args_with_list_of_dicts_to_pydantic_models():
"""Test _preprocess_args converts list of dicts to list of Pydantic models."""
tool = FunctionTool(function_with_list_of_pydantic_models)

input_args = {
"users": [
{"name": "Alice", "age": 30, "email": "alice@example.com"},
{"name": "Bob", "age": 25},
{"name": "Charlie", "age": 35, "email": "charlie@example.com"},
]
}

processed_args = tool._preprocess_args(input_args)

# Check that the list of dicts was converted to a list of Pydantic models
assert "users" in processed_args
users = processed_args["users"]
assert isinstance(users, list)
assert len(users) == 3

# Check each element is a Pydantic model with correct data
assert isinstance(users[0], UserModel)
assert users[0].name == "Alice"
assert users[0].age == 30
assert users[0].email == "alice@example.com"

assert isinstance(users[1], UserModel)
assert users[1].name == "Bob"
assert users[1].age == 25
assert users[1].email is None

assert isinstance(users[2], UserModel)
assert users[2].name == "Charlie"
assert users[2].age == 35
assert users[2].email == "charlie@example.com"


def test_preprocess_args_with_optional_list_of_pydantic_models_none():
"""Test _preprocess_args handles None for optional list parameter."""
tool = FunctionTool(function_with_optional_list_of_pydantic_models)

input_args = {"users": None}

processed_args = tool._preprocess_args(input_args)

# Check that None is preserved
assert "users" in processed_args
assert processed_args["users"] is None


def test_preprocess_args_with_optional_list_of_pydantic_models_with_data():
"""Test _preprocess_args converts list for optional list parameter."""
tool = FunctionTool(function_with_optional_list_of_pydantic_models)

input_args = {
"users": [
{"name": "Alice", "age": 30},
{"name": "Bob", "age": 25},
]
}

processed_args = tool._preprocess_args(input_args)

# Check conversion
assert "users" in processed_args
users = processed_args["users"]
assert len(users) == 2
assert all(isinstance(user, UserModel) for user in users)
assert users[0].name == "Alice"
assert users[1].name == "Bob"


def test_preprocess_args_with_list_skips_invalid_items():
"""Test _preprocess_args skips items that fail validation."""
tool = FunctionTool(function_with_list_of_pydantic_models)

input_args = {
"users": [
{"name": "Alice", "age": 30},
{"name": "Invalid"}, # Missing required 'age' field
{"name": "Bob", "age": 25},
]
}

processed_args = tool._preprocess_args(input_args)

# Check that invalid item was skipped
assert "users" in processed_args
users = processed_args["users"]
assert len(users) == 2 # Only 2 valid items
assert users[0].name == "Alice"
assert users[0].age == 30
assert users[1].name == "Bob"
assert users[1].age == 25

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