Product Monitoring & Innovation

@tool(name="ProductMonitoringTool", description="Analyze product performance and innovation trends for aging insurance products(csv_path, product, metric, start_year, end_year).", show_result=True)
def ProductMonitoringTool(
    csv_path: str = None,
    product: str = None,
    metric: str = None,
    start_year: int = 2020,
    end_year: int = 2021,
) -> str:
    """
    Analyze product performance and innovation trends for aging insurance products.

    Args:
        csv_path: Path to CSV with historical product metrics.
        product: Filter results for a specific product. Defaults to None (all products).
        metric: Filter by metric(e.g. LossRatio, PersistencyRate, ClaimsFrequency, ClaimsSeverity). Defaults to None (all metrics).
        start_year: Starting year for analysis. Defaults to 2020.
        end_year: Ending year for analysis. Defaults to 2021.

    Returns:
        str: JSON string with filtered metrics and trends.
    """
    csv_path = csv_path or _path("agent6.csv")
    try:
        df = pd.read_csv(csv_path)
        if product:
            df = df[df["ProductName"].str.lower() == product.lower()]
        if metric:
            df = df[df["Metric"].str.lower() == metric.lower()]
        df = df[(df["Year"] >= start_year) & (df["Year"] <= end_year)]
        return json.dumps(df.to_dict(orient="records"), indent=2)
    except Exception as e:
        return json.dumps({"error": str(e)})