I'll systematically check for data quality issues across the dataset. Let me start by examining the key tables for outliers, impossible values, and inconsistencies. {"action": "tool_call", "reasoning": "First, I'll check for outliers and impossible values in the physical measurements (height, reach, age) and fight statistics across the main cleandata table. I'll look for values outside reasonable ranges for boxing.", "tool": "query_sql", "args": {"sql": "SELECT COUNT(*) as total_rows, COUNT(CASE WHEN f_boxer_age < 18 OR f_boxer_age > 50 THEN 1 END) as suspicious_f_age, COUNT(CASE WHEN s_boxer_age < 18 OR s_boxer_age > 50 THEN 1 END) as suspicious_s_age, COUNT(CASE WHEN f_boxer_height < 150 OR f_boxer_height > 230 THEN 1 END) as suspicious_f_height, COUNT(CASE WHEN f_boxer_reach < 150 OR f_boxer_reach > 250 THEN 1 END) as suspicious_f_reach, COUNT(CASE WHEN s_boxer_reach < 150 OR s_boxer_reach > 250 THEN 1 END) as suspicious_s_reach, COUNT(CASE WHEN f_boxer_won < 0 OR f_boxer_lost < 0 OR f_boxer_drawn < 0 THEN 1 END) as negative_f_record, COUNT(CASE WHEN s_boxer_won < 0 OR s_boxer_lost < 0 OR s_boxer_drawn < 0 THEN 1 END) as negative_s_record, COUNT(CASE WHEN f_boxer_KOs > f_boxer_won THEN 1 END) as f_kos_exceed_wins, COUNT(CASE WHEN s_boxer_KOs > s_boxer_won THEN 1 END) as s_kos_exceed_wins FROM raw.cleandata", "purpose": "Check for outliers and impossible values in physical measurements and fight records", "source": "raw"}}