{
    "action": "tool_call",
    "reasoning": "I have comprehensive profiling data showing Age nulls (20%), Cabin nulls (77%), and suspicious zero fares. I need to quantify the zero-fare issue and check for extreme outliers in Fare to complete the quality assessment.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) FILTER (WHERE Fare = 0) AS zero_fares, COUNT(*) FILTER (WHERE Fare > 200) AS extreme_high_fares, COUNT(*) FILTER (WHERE Fare > 100 AND Fare <= 200) AS high_fares, COUNT(*) FILTER (WHERE Age < 1) AS infants, COUNT(*) FILTER (WHERE Age > 75) AS very_elderly, COUNT(*) FILTER (WHERE Survived NOT IN (0, 1)) AS invalid_survived, COUNT(*) FILTER (WHERE Pclass NOT IN (1, 2, 3)) AS invalid_pclass FROM raw.titanic",
        "purpose": "Identify specific data quality issues: zero fares, extreme outliers, impossible values",
        "source": "raw"
    }
}