{
    "action": "tool_call",
    "reasoning": "I need to identify the most important numeric metric. Looking at the evidence, I have several numeric metrics: f_boxer_won, f_boxer_lost, f_boxer_KOs, s_boxer_won, s_boxer_lost, s_boxer_KOs, ages, heights, reaches, and rankings. From step-3, I can see the statistics for some key metrics. The most important metric in a boxing dataset would likely be wins (f_boxer_won) as it represents career success. However, I should also consider KOs (knockouts) as they're a key performance indicator in boxing. Let me check the distribution and importance by looking at which metric has the most variation and significance. From step-3: f_boxer_won ranges 0-64 with avg 24.5, f_boxer_KOs ranges 0-53 with avg 20.8. Wins appear to be the primary success metric. Let me query for the highest and lowest records by f_boxer_won to show the extremes.",
    "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, s_boxer_won, s_boxer_lost FROM raw.cleandata ORDER BY f_boxer_won DESC LIMIT 10",
        "purpose": "Get the top 10 records with highest f_boxer_won values",
        "source": "raw"
    }
}