ai-agents-tool-schema-validati-pydantic-validationerror-when-llm-returns-a-dict-f-c4dc6277

THEN Ensure tool parameters are defined with primitive types (e.g., str, int). In the tool function, add type coercion logic (e.g., isinstance check) to safely extract the expected value if the LLM returns a dict or unexpected structure. Also verify the tool schema matches the model's expected format.

@tool
def GetNewQuestion(category: str) -> str:
    """Fetch a new interview question based on the provided category."""
    if isinstance(category, dict):
        category = category.get("category", "general")
    return f"What is your experience with {category}?"