{
    "action": "tool_call",
    "reasoning": "I have good evidence on the natural groupings already: results (win_A 83%, win_B 10.6%, draw 6.4%), decisions (10 types led by PTS 27.9%, TKO 23.2%, KO 18.3%), and stance matchups (orthodox-orthodox 49.1%, missing stances 40.4%, southpaw-southpaw 10.5%). I also know data completeness varies (age 62%, height 27%, reach 6%, weight 24%). Now I need to explore age distribution properly (my previous attempt failed due to window function syntax). Let me fix that query to see age segments for fighter A.",
    "tool": "query_sql",
    "purpose": "Get age distribution for fighter A to identify natural age segments",
    "sql": "WITH total AS (SELECT COUNT(*) AS n FROM raw.boxing_matches WHERE age_A IS NOT NULL) SELECT CASE WHEN age_A < 25 THEN 'under_25' WHEN age_A BETWEEN 25 AND 30 THEN '25-30' WHEN age_A BETWEEN 31 AND 35 THEN '31-35' WHEN age_A > 35 THEN 'over_35' END AS age_group, COUNT(*) AS match_count, ROUND(100.0 * COUNT(*) / (SELECT n FROM total), 1) AS pct FROM raw.boxing_matches WHERE age_A IS NOT NULL GROUP BY age_group ORDER BY match_count DESC"
}