Annotated parameters breaks `_preprocess_args` when using recursive type aliases or any unresolvable forward reference

[MarlzRana]

🔴 Required Information

Describe the Bug:
Annotated parameters breaks _preprocess_args when using recursive type aliases or any unresolvable forward reference on the fallback path when we are unable to get_type_hints() in src/google/adk/tools/function_tool.py, fixed by PR: #6879

Steps to Reproduce:

1. Declare a tool containing parameter with a recursive type:

Recursive = Union[int, str, list["Recursive"]]

def fn(
    user: Annotated[UserModel, Field(description="A user")],
    data: Recursive = None,
) -> dict:
  return {"name": user.name, "type": type(user).__name__}

2. Try and call the tool

Expected Behavior:

user is converted to a UserModel instance before the function runs (as it is on the normal path where get_type_hints() succeeds and strips the Annotated wrapper). The tool executes and returns:

{"name": "Alice", "type": "UserModel"}

Observed Behavior:

get_type_hints() raises NameError, so _preprocess_args falls back to the raw param.annotation, which is still Annotated[UserModel, ...]wrapped. None of the conversion branches recognise the wrapper, so the dict is passed through unconverted:

# tool._preprocess_args({"user": {"name": "Alice", "age": 30}})
{'user': {'name': 'Alice', 'age': 30}}   # still a dict, not a UserModel

When the tool body then accesses user.name, it raises:

Traceback (most recent call last):
  ...
  File "src/google/adk/tools/function_tool.py", line 353, in run_async
    return await self._invoke_callable(self.func, args_to_call)
  File "src/google/adk/tools/function_tool.py", line 378, in _invoke_callable
    return target(**args_to_call)
  File ".../your_tool.py", line 19, in fn
    return {"name": user.name, "type": type(user).__name__}
           ^^^^^^^^^
AttributeError: 'dict' object has no attribute 'name'

(The line numbers in function_tool.py are relative to the current main.)

Environment Details:

• ADK Library Version: 2.7.1
• Desktop OS: MacOS
• Python Version: 3.13

Model Information:

• Are you using LiteLLM: No
• Which model is being used: gemini-3.7-flash

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🟡 Optional Information

Regression:
Not a regression.

How often has this issue occurred?:

• Always (100%)