{
    "action": "tool_call",
    "reasoning": "The evidence shows the dataset has strong natural segmentation by result (83% win_A, 10.6% win_B, 6.4% draw) and decision types (10 categories with PTS/TKO/KO being the largest). I also see stance matchups create 3 main groups. However, I should explore whether fighter characteristics (age, weight, experience) create meaningful segments beyond these categorical splits. Let me check if there are natural clusters in the continuous variables that could define segments.",
    "tool": "query_sql",
    "args": {
        "sql": "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' ELSE 'over_35' END AS age_group, COUNT(*) AS match_count, ROUND(100.0 * COUNT(*) / SUM(COUNT(*) OVER ()), 1) AS pct FROM raw.boxing_matches WHERE age_A IS NOT NULL GROUP BY age_group ORDER BY match_count DESC",
        "purpose": "Check if fighter age creates natural segments",
        "source": "raw"
    }
}