{
    "action": "tool_call",
    "reasoning": "From the evidence, I've identified that the 'No' column contains boxer names (the most important category) and 'Days' contains reign durations (the most important metric). However, the Days column has messy VARCHAR data with commas, dashes, and text. I need to query the actual numeric reign durations by boxer, cleaning the Days column to extract only valid numbers, removing commas, and aggregating by the No column (boxer names).",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT No as Boxer, COUNT(*) as num_reigns, SUM(CAST(REPLACE(Days, ',', '') AS INTEGER)) as total_days, ROUND(AVG(CAST(REPLACE(Days, ',', '') AS INTEGER)), 1) as avg_days_per_reign FROM raw.championsdataset WHERE Days NOT IN ('def.', '–', '−', '', 'draw vs.') AND Days NOT LIKE '%def.%' AND Days NOT LIKE '%draw%' AND LENGTH(Days) <= 6 GROUP BY No ORDER BY total_days DESC LIMIT 20",
        "purpose": "Show how the key metric (Days - reign duration) varies across the key category (No - boxer names), aggregating total and average days per boxer",
        "source": "raw"
    }
}