{
    "action": "tool_call",
    "reasoning": "The question asks me to identify the most important metric and category, then show variation. From the overview, this is a medal history dataset. The most important metric is clearly medal count (the core measure of success), and the most important category is country (national performance is the primary lens for Olympic analysis). I need to compute medal counts by country. The card shows no gold tables exist for this, so I'll query raw.* directly to get total medals per country, ordered by count.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT country, COUNT(*) as total_medals, SUM(CASE WHEN medal = 'Gold' THEN 1 ELSE 0 END) as gold_medals, SUM(CASE WHEN medal = 'Silver' THEN 1 ELSE 0 END) as silver_medals, SUM(CASE WHEN medal = 'Bronze' THEN 1 ELSE 0 END) as bronze_medals FROM raw.olympic_table_tennis_medals_1988_2024 GROUP BY country ORDER BY total_medals DESC",
        "purpose": "Count total medals and breakdown by medal type for each country to show how the most important metric (medal count) varies across the most important category (country)",
        "source": "raw"
    }
}