{
    "action": "tool_call",
    "reasoning": "I have evidence showing the dataset contains boxing match records with results (win_A 83%, win_B 10.6%, draw 6.4%) and stance breakdowns (orthodox 190k matches, southpaw 41k, blank/other 156k). To identify the 'most important' metric and category, I need to determine which metric is most predictive of match outcomes. The winning record (won_A, won_B) is likely the most important metric as it represents career success and should correlate with match results. I'll query the gold table that breaks down by both result AND stance to see how fighter A's winning record varies across the most meaningful dimensions.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT result, stance_A, row_count, won_A__avg, won_A__median FROM gold.boxing_matches_by_stance_A_result ORDER BY result, stance_A",
        "purpose": "Get fighter A's average winning record broken down by match result and stance to identify patterns",
        "source": "gold"
    }
}