{
    "action": "tool_call",
    "reasoning": "I need to determine which numeric metric is most important in this boxing dataset. Looking at the evidence, I can see measures like wins, losses, draws, KOs, age, height, reach, and weight for both fighters. The 'result' column (win_A, win_B, draw) appears to be the outcome variable. In boxing analytics, the most important metric is typically the win-loss record, as it directly measures a fighter's success. I should analyze the distribution and significance of the key metrics to identify which is most central to the dataset's purpose. Let me query to see the correlation between various metrics and match outcomes, and examine which metrics have the most complete data and variation.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_matches, COUNT(CASE WHEN result = 'win_A' THEN 1 END) as wins_A, COUNT(CASE WHEN result = 'win_B' THEN 1 END) as wins_B, COUNT(CASE WHEN result = 'draw' THEN 1 END) as draws, AVG(won_A) as avg_wins_A, AVG(won_B) as avg_wins_B, AVG(lost_A) as avg_losses_A, AVG(lost_B) as avg_losses_B, AVG(kos_A) as avg_kos_A, AVG(kos_B) as avg_kos_B, STDDEV(won_A) as stddev_wins_A, STDDEV(won_B) as stddev_wins_B FROM raw.boxing_matches",
        "purpose": "Analyze the distribution of key metrics and match outcomes to identify the most important numeric measure",
        "source": "raw"
    }
}