{
    "action": "tool_call",
    "reasoning": "I have comprehensive profiling data showing several potential quality issues: 177 missing Age values (20%), 687 missing Cabin values (77%), zero fares, and extreme fare outliers (max 512 vs median 14.45). I need to quantify the zero-fare issue and check for any impossible values in the binary/categorical fields. Let me query for: (1) count of zero fares, (2) distribution of extreme fares, (3) any out-of-range values in Survived (should be 0 or 1), and (4) any unusual Pclass values (should be 1, 2, or 3).",
    "tool": "query_sql",
    "purpose": "Identify specific data quality issues: zero fares, extreme outliers, and out-of-range categorical values",
    "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 Survived NOT IN (0, 1)) as invalid_survived, COUNT(*) FILTER (WHERE Pclass NOT IN (1, 2, 3)) as invalid_pclass, COUNT(*) FILTER (WHERE Sex NOT IN ('male', 'female')) as invalid_sex, COUNT(*) FILTER (WHERE Age < 0) as negative_age, COUNT(*) FILTER (WHERE Age > 100) as implausible_age FROM raw.titanic",
        "source": "raw"
    }
}