{
    "action": "tool_call",
    "reasoning": "I need to identify the most important numeric metric. Looking at the evidence, I can see several numeric metrics: f_boxer_won, f_boxer_lost, f_boxer_KOs, s_boxer_won, s_boxer_lost. From step-3, I have summary statistics showing f_boxer_won ranges from 0-64 with avg 24.5, f_boxer_lost ranges 0-5 with avg 0.94, f_boxer_KOs ranges 0-53 with avg 20.8, and s_boxer_won ranges 2-85 with avg 22.9. The most important metric in boxing is typically the win count, as it represents career success. Between f_boxer_won (max 64) and s_boxer_won (max 85), I should examine both. However, I notice the data shows many records with 0 values for f_boxer_won in steps 2 and 6, which seems like data quality issues. Let me query to get the actual highest and lowest records by f_boxer_won, excluding the suspicious 0 values.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT f_boxer, s_boxer, f_boxer_result, f_boxer_won, f_boxer_lost, f_boxer_KOs, f_boxer_age FROM raw.cleandata WHERE f_boxer_won > 0 ORDER BY f_boxer_won DESC LIMIT 10",
        "purpose": "Get the top 10 records by f_boxer_won (career wins), excluding suspicious 0 values"
    }
}