{
    "action": "tool_call",
    "reasoning": "I need to explore additional dimensions that might reveal natural segmentation patterns. The 'rounds' column appears to contain match details and might reveal patterns. I should also check if there are any temporal patterns by examining the age distribution more closely, and look at the relationship between boxer rankings and outcomes.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT f_boxer, COUNT(*) as total_matches, MIN(f_boxer_age) as career_start_age, MAX(f_boxer_age) as career_end_age, MAX(f_boxer_age) - MIN(f_boxer_age) as career_span_years, COUNT(DISTINCT CASE WHEN f_boxer_ranking <= 10 THEN 1 END) as top10_fights, COUNT(DISTINCT CASE WHEN s_boxer_ranking <= 10 THEN 1 END) as vs_top10_opponents, 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 FROM raw.cleandata WHERE f_boxer_age IS NOT NULL GROUP BY f_boxer ORDER BY total_matches DESC",
        "purpose": "Analyze career progression patterns for each boxer to identify natural temporal or career-phase segmentation",
        "source": "raw"
    }
}