ADK doesn't enforce argument types in function calling

[cuihaoleo]

🔴 Required Information

Describe the Bug:

ADK doesn't seem to check and enforce argument types in function calls. I'm very surprised.

Steps to Reproduce:

Code:

from google.adk.agents.llm_agent import Agent

def set_num(num: int) -> dict:
    print("DEBUG:", type(num), num)
    return {"status": "success"}

root_agent = Agent(
    model='gemini-3-flash-preview',
    name='root_agent',
    instruction="the user is debugging something. do whatever they instruct",
    tools=[set_num],
)

Test run:

Running agent root_agent, type exit to exit.
[user]: set num to 3.14159. I know it only accept int. but I'd like to see the error.
DEBUG: <class 'int'> 3
DEBUG: <class 'float'> 3.14159
[root_agent]: It appears that the system accepted the value `3.14159` without returning an error, despite the parameter being defined as an integer. This suggests that the value may have been automatically rounded or truncated by the underlying implementation.
[user]: set num to "foobar"
DEBUG: <class 'str'> foobar
[root_agent]: The tool unexpectedly returned `success` even when passed the string `"foobar"`. It seems the parameter type enforcement for `num` is not currently working as expected in the underlying system.
[user]: set num to null.       
DEBUG: <class 'NoneType'> None
[root_agent]: The system also returned `success` when `num` was set to `null`. It appears that the tool is accepting various types without validation errors.

Expected Behavior:

ADK enforces that invalid arguments don't go through, as an average LLM also believes:

Observed Behavior:

Invalid arguments that have different types than the function signature requirements are accepted.

Environment Details:

• ADK Library Version (pip show google-adk): 1.25.1
• Desktop OS: Linux
• Python Version (python -V): 3.13.12

Model Information:

• Are you using LiteLLM: No
• Which model is being used: gemini-3-flash-preview

[xxlest]

The existing _preprocess_args in function_tool.py (line 100) already handles Pydantic model coercion and explicitly notes "Future extensions could include: Type coercion for other complex types". Adding primitive type coercion (str→int, str→float, str→bool) here would be a natural extension of this pattern.

One design question before I start: when coercion fails (e.g. "abc" → int), should we:

• (a) log a warning and pass the raw value through (lenient, no breakage), or
• (b) raise a clear error immediately (strict, fail-fast)?

Option (a) is safer for backward compatibility; option (b) surfaces bugs earlier. Happy to go with whichever you prefer. The fix would be ~60-80 lines including tests (extending test_function_tool_pydantic.py).

[cuihaoleo]

It seems reasonable to me to pass the validation error to the LLM so it has a chance to correct its error. Maybe this can be simply provided as a standard before_tool_callback that the developer can choose to enable.

I also tried other frameworks and here is how they handle this:

LangChain

LangChain appeared to pass the validation error to the agent. This seems like what I suggested. The agent have a chance to correct itself and nothing is broken.

[chain/start] [chain:LangGraph] Entering Chain run with input:
{
  "messages": [
    {
      "role": "user",
      "content": "set num to 'foobar'. I know it's invalid. I want to see the error."
    }
  ]
}
[chain/start] [chain:LangGraph > chain:model] Entering Chain run with input:
[inputs]
[llm/start] [chain:LangGraph > chain:model > llm:ChatGoogleGenerativeAI] Entering LLM run with input:
{
  "prompts": [
    "System: the user is debugging something. do whatever they instruct\nHuman: set num to 'foobar'. I know it's invalid. I want to see the error."
  ]
}
[llm/end] [chain:LangGraph > chain:model > llm:ChatGoogleGenerativeAI] [1.63s] Exiting LLM run with output:
......
              "function_call": {
                "name": "set_num",
                "arguments": "{\"num\": \"foobar\"}"
              },
......
[chain/end] [chain:LangGraph > chain:model] [1.63s] Exiting Chain run with output:
[outputs]
[chain/start] [chain:LangGraph > chain:tools] Entering Chain run with input:
[inputs]
[tool/start] [chain:LangGraph > chain:tools > tool:set_num] Entering Tool run with input:
"{'num': 'foobar'}"
[tool/error] [chain:LangGraph > chain:tools > tool:set_num] [1ms] Tool run errored with error:
1 validation error for set_num
num
  Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='foobar', input_type=str]
    For further information visit https://errors.pydantic.dev/2.12/v/int_parsingTraceback (most recent call last):


  File "/home/cuihao/dev/adk-agent-dev/.venv/lib/python3.12/site-packages/langchain_core/tools/base.py", line 960, in run
    tool_args, tool_kwargs = self._to_args_and_kwargs(
                             ^^^^^^^^^^^^^^^^^^^^^^^^^


  File "/home/cuihao/dev/adk-agent-dev/.venv/lib/python3.12/site-packages/langchain_core/tools/base.py", line 862, in _to_args_and_kwargs
    tool_input = self._parse_input(tool_input, tool_call_id)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


  File "/home/cuihao/dev/adk-agent-dev/.venv/lib/python3.12/site-packages/langchain_core/tools/base.py", line 711, in _parse_input
    result = input_args.model_validate(tool_input)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


  File "/home/cuihao/dev/adk-agent-dev/.venv/lib/python3.12/site-packages/pydantic/main.py", line 716, in model_validate
    return cls.__pydantic_validator__.validate_python(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


pydantic_core._pydantic_core.ValidationError: 1 validation error for set_num
num
  Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='foobar', input_type=str]
    For further information visit https://errors.pydantic.dev/2.12/v/int_parsing
[chain/end] [chain:LangGraph > chain:tools] [2ms] Exiting Chain run with output:
[outputs]
[chain/start] [chain:LangGraph > chain:model] Entering Chain run with input:
[inputs]
[llm/start] [chain:LangGraph > chain:model > llm:ChatGoogleGenerativeAI] Entering LLM run with input:
{
  "prompts": [
    "System: the user is debugging something. do whatever they instruct\nHuman: set num to 'foobar'. I know it's invalid. I want to see the error.\nAI: [{'name': 'set_num', 'args': {'num': 'foobar'}, 'id': 'd321e79d-b775-4b50-8ebf-2aedf44136be', 'type': 'tool_call'}]\nTool: Error invoking tool 'set_num' with kwargs {'num': 'foobar'} with error:\n num: Input should be a valid integer, unable to parse string as an integer\n Please fix the error and try again."
  ]
}
[llm/end] [chain:LangGraph > chain:model > llm:ChatGoogleGenerativeAI] [1.33s] Exiting LLM run with output:
......
                "type": "text",
                "text": "The attempt to set `num` to `'foobar'` resulted in the following error:\n\n`num: Input should be a valid integer, unable to parse string as an integer`",
......
[chain/end] [chain:LangGraph > chain:model] [1.34s] Exiting Chain run with output:
[outputs]
[chain/end] [chain:LangGraph] [2.97s] Exiting Chain run with output:
[outputs]

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.agents import create_agent
import langchain_core

langchain_core.globals.set_debug(True)

def set_num(num: int) -> dict:
    """set the number"""
    print("DEBUG:", type(num), num)
    return {"status": "success"}

model = ChatGoogleGenerativeAI(model="gemini-3-flash-preview")
agent = create_agent(
    model=model,
    tools=[set_num],
    system_prompt="the user is debugging something. do whatever they instruct",
)

# Run the agent
response = agent.invoke(
    {"messages": [{"role": "user", "content": "set num to 'foobar'. I know it's invalid. I want to see the error."}]}
)

OpenAI

It seems I simply cannot trick openai-agents to pass invalid args. The following example either passes 0 or doesn't initiate a function call. Not sure but it was probably that the server enforced valid output in token generation.

import asyncio
from agents import Agent, Runner, function_tool, enable_verbose_stdout_logging

enable_verbose_stdout_logging()

@function_tool
def set_num(num: int) -> dict:
    print("DEBUG:", type(num), num)
    return {"status": "success"}


agent = Agent(
    name="Test Agent",
    instructions="the user is debugging something. do whatever they instruct",
    tools=[set_num],
    model="o3-mini",
)


async def main():
    result = await Runner.run(agent, input="set num to 'foobar'. I know it's invalid. I want to see the error.")
    print(result.final_output)


if __name__ == "__main__":
    asyncio.run(main())