{
    "action": "tool_call",
    "reasoning": "I've found several data quality issues already: 1) 1480 pipeline deals reference 'GTXPro' product which doesn't exist in products table, 2) 1425 pipeline deals reference accounts not in accounts table, 3) Logical null patterns align with deal_stage (Prospecting/Engaging have no close data, Won/Lost do). Now I need to check for outliers in the numeric fields. I'll examine the accounts table for extreme outliers in revenue and employees using the statistics I already have (p01/p99 ranges), and check for any text formatting inconsistencies in key dimension fields.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT account, revenue, employees, year_established, CASE WHEN revenue > 8734.80 THEN 'revenue_outlier_high' WHEN revenue < 34.99 THEN 'revenue_outlier_low' ELSE 'revenue_ok' END AS revenue_flag, CASE WHEN employees > 22532 THEN 'employees_outlier_high' WHEN employees < 82 THEN 'employees_outlier_low' ELSE 'employees_ok' END AS employees_flag FROM raw.accounts WHERE revenue > 8734.80 OR revenue < 34.99 OR employees > 22532 OR employees < 82 ORDER BY revenue DESC",
        "purpose": "Identify outlier accounts beyond p01/p99 thresholds in revenue or employees",
        "source": "raw"
    }
}