{
    "action": "tool_call",
    "reasoning": "I need to identify the most important numeric metric. Looking at the evidence, I have several numeric metrics: f_boxer_won, f_boxer_lost, f_boxer_KOs, s_boxer_won, and others. The dataset appears to be about boxing matches. The most important metric would likely be the win/loss record or KO count. However, I notice from step-2 that there are records with 0 values for won/lost/KOs, which seems odd. Let me query to understand the actual distribution better and identify which metric has the most meaningful variation and importance. I'll look at the primary outcome metric - f_boxer_result - and the associated performance metrics to determine which is most central to understanding boxing performance.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT f_boxer, f_boxer_result, f_boxer_won, f_boxer_lost, f_boxer_KOs, s_boxer, s_boxer_won, s_boxer_lost FROM raw.cleandata WHERE f_boxer_won > 0 ORDER BY f_boxer_won DESC LIMIT 10",
        "purpose": "Get top records by f_boxer_won to see the highest performing boxers and understand the data quality",
        "source": "raw"
    }
}