Stop Guessing: How to Handle Missing Parameters in AI Agent Tool Calls

One of the most overlooked challenges in building AI agents isn't planning.

It's knowing when not to execute a tool call.

Consider this user request: "Schedule a meeting with Alex tomorrow."

The intent is clear, but several important parameters may be missing:

If it calls the calendar API with incomplete arguments, the operation might fail entirely.

Ready to execute: All required parameters are available, and the action is authorized.

Needs clarification: Information required for execution is missing or ambiguous.

Cannot proceed: The request is invalid, unsupported, or unauthorized.

This decision should happen before executing a state-changing operation.

We can represent those requirements using Pydantic: from datetime import datetime from pydantic import BaseModel, ValidationError, model_validator

class MeetingRequest(BaseModel): participant: str start: datetime end: datetime

@model_validator(mode="after") def check_time_range(self): if self.end <= self.start: raise ValueError("End time must be after start time") return self Enter fullscreen mode Exit fullscreen mode def validate_request(data): try: request = MeetingRequest.model_validate(data) return {"status": "ready", "request": request} except ValidationError as exc: return { "status": "needs_review", "errors": exc.errors(include_url=False) } The validation step detects missing fields and invalid time ranges.

However, validation errors should not automatically trigger a tool retry.

The agent must first determine whether the error can be resolved from authorized context or whether it needs to ask the user.

After receiving the answer, it can rebuild the request and validate it again.

User Request → Extract Parameters → Validate → Clarify if Needed → Authorize → Execute → Verify

This helps separate natural-language interpretation from external execution.

An agent may be able to use an existing preference, such as a previously authorized default meeting duration.

But there's an important difference between:

Good clarification logic is selective, not excessive.

An API may time out, a calendar event may be rejected, or the wrong external state may be updated.

That's why parameter validation is only one part of reliable execution.

The workflow also needs error handling, authorization checks, and outcome verification.

Through local diagnostic testing, we've investigated failure scenarios involving missing calendar parameters.

These experiences highlight why agent systems need careful validation before interacting with external tools.

HEELO is still under development, and these design considerations are part of our ongoing exploration.

A reliable AI agent shouldn't just know how to call a tool. It should know when it has enough information to call it correctly.

Discussion How do you handle missing tool parameters in your AI agent projects?

Do you use JSON Schema validation, Pydantic, LLM-based clarification, or a combination of these approaches?

I'd love to hear what has worked in your implementations.