Best Practice when collecting specific information from a user

[gmyrianthous]

Hey everyone,

I wanted to get your views for a specific use-case I'm considering and would be useful to understand from other people how they'd approach this problem.

Let's say, I have a tool that performs some sort of calculations:

def my_tool(arg_1: int, arg_2: float, arg_3: str):
    # Perform some calculations to determine the result
    result = ....
    return result

Now the input of this tool, should be coming from user, who is expected to provide this info, in a Q/A fashion. i.e. the agent should ask questions to the user, in order to collect this info (arg_1, arg_2, arg_3, ...). As the user will start providing this info, the agent should somehow retain the fact that some of the information is provided already, and should stop asking further questions if all info is provided by the user.

What is the best way for achieving this? Would it make sense to construct an object, that is gradually filled in, within the session's state? Any alternatives other than just relying fully on the prompt?

[dexhunter]

Would it make sense to construct an object, that is gradually filled in, within the session's state?

Keep arg_1, arg_2, and arg_3 in session state as they arrive. A collection tool can validate each new value, merge it into tool_context.state["inputs"], and return the fields still missing. The calculation tool should read the collected state and refuse to run until all required values are present and valid.

After validating the incoming fields, the collection tool can do this:

collected = dict(tool_context.state.get("inputs", {}))
collected.update(validated_fields)
tool_context.state["inputs"] = collected

required = ("arg_1", "arg_2", "arg_3")
missing = [key for key in required if collected.get(key) is None]
return {"collected": collected, "missing": missing}

The is None check keeps valid 0 and 0.0 values from being marked missing. Assign the updated dictionary back through context state so ADK tracks the change; mutating a nested dictionary in place is not enough to rely on.

In the agent instructions, ask for the returned missing fields and clarify ambiguous answers. Keep the validation and completeness checks in Python, and reuse the same session ID across turns so previously collected inputs remain available.