{
    "action": "tool_call",
    "reasoning": "I need to systematically check for data quality issues. Starting with the measures and key dimensions, I'll look for: 1) Outliers in numeric fields (extreme values, suspicious patterns), 2) Impossible values (negatives where they shouldn't be, dates in the future), 3) Inconsistent formats in text fields, 4) Mixed units or scaling issues. Let me begin by profiling the key numeric measures to identify outliers and impossible values.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_rows, COUNT(CASE WHEN \"Max OP/s (log)\" < 0 THEN 1 END) as negative_max_ops, COUNT(CASE WHEN \"H100 equivalents\" < 0 THEN 1 END) as negative_h100, COUNT(CASE WHEN \"Chip quantity (primary)\" < 0 THEN 1 END) as negative_chip_qty, COUNT(CASE WHEN \"Power Capacity (MW)\" < 0 THEN 1 END) as negative_power, COUNT(CASE WHEN \"Hardware Cost\" < 0 THEN 1 END) as negative_cost, MIN(\"First Operational Date\") as earliest_date, MAX(\"First Operational Date\") as latest_date, COUNT(CASE WHEN \"First Operational Date\" > CURRENT_DATE THEN 1 END) as future_dates, MIN(\"Power Capacity (MW)\") as min_power, MAX(\"Power Capacity (MW)\") as max_power, MIN(\"Hardware Cost\") as min_cost, MAX(\"Hardware Cost\") as max_cost, MIN(\"Chip quantity (primary)\") as min_chips, MAX(\"Chip quantity (primary)\") as max_chips, MIN(\"H100 equivalents\") as min_h100, MAX(\"H100 equivalents\") as max_h100 FROM raw.ai_supercomputers",
        "purpose": "Profile key numeric measures for outliers, impossible values, and data quality issues",
        "source": "raw"
    }
}