{
    "action": "tool_call",
    "reasoning": "I need to identify which boxers appear most frequently in the dataset and calculate their key performance metrics. Since f_boxer has only ~12 distinct values (vs s_boxer with ~93), f_boxer represents the featured boxers whose careers are being tracked. I'll query to find the most important boxers by match count and calculate their win percentage and KO rate - the two most critical boxing metrics.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT f_boxer, COUNT(*) as total_matches, SUM(CASE WHEN f_boxer_result = 'won' THEN 1 ELSE 0 END) as wins, SUM(CASE WHEN f_boxer_result = 'lost' THEN 1 ELSE 0 END) as losses, ROUND(100.0 * SUM(CASE WHEN f_boxer_result = 'won' THEN 1 ELSE 0 END) / COUNT(*), 1) as win_percentage, AVG(f_boxer_KOs) as avg_KOs, MAX(f_boxer_won) as career_wins, MAX(f_boxer_lost) as career_losses FROM raw.cleandata GROUP BY f_boxer ORDER BY total_matches DESC",
        "purpose": "Identify the featured boxers, their match counts, and key performance metrics (win percentage and KO stats)",
        "source": "raw"
    }
}