{
    "action": "tool_call",
    "reasoning": "I have basic statistics from step-0 showing ranges and distributions for numeric columns. Now I need to check for specific data quality issues: (1) text columns for format inconsistencies, (2) categorical columns for unexpected values, (3) missing data patterns beyond what step-0 showed, and (4) logical inconsistencies. Let me start by examining the categorical and text columns for format issues and unexpected values.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_rows, COUNT(DISTINCT Sex) as sex_distinct, COUNT(DISTINCT Embarked) as embarked_distinct, COUNT(DISTINCT Pclass) as pclass_distinct, SUM(CASE WHEN Sex IS NULL THEN 1 ELSE 0 END) as sex_nulls, SUM(CASE WHEN Embarked IS NULL THEN 1 ELSE 0 END) as embarked_nulls, SUM(CASE WHEN Cabin IS NULL THEN 1 ELSE 0 END) as cabin_nulls, SUM(CASE WHEN Name IS NULL THEN 1 ELSE 0 END) as name_nulls, SUM(CASE WHEN Ticket IS NULL THEN 1 ELSE 0 END) as ticket_nulls FROM raw.titanic",
        "purpose": "Check for nulls in categorical/text columns and verify expected distinct counts",
        "source": "raw"
    }
}