{
    "action": "tool_call",
    "reasoning": "I've seen the basic win/loss breakdown, but to identify natural segments I should explore multiple dimensions. Let me check the distribution of boxers by stance, age groups, and experience levels to see if there are clear clusters or natural groupings in the data.",
    "tool": "query_sql",
    "purpose": "Explore multiple segmentation dimensions: stance distribution, age groups, experience levels (wins), and ranking tiers",
    "args": {
        "sql": "SELECT f_boxer_stance, COUNT(*) as match_count, AVG(f_boxer_age) as avg_age, AVG(f_boxer_won) as avg_wins, AVG(f_boxer_height) as avg_height FROM raw.cleandata WHERE f_boxer_stance NOT LIKE '%cm%' GROUP BY f_boxer_stance ORDER BY match_count DESC",
        "source": "raw"
    }
}