{
    "action": "tool_call",
    "reasoning": "The previous query failed because TRTDURD is VARCHAR, not numeric. I need to check for data quality issues systematically. First, I'll examine the temporal columns for format consistency and impossible date sequences, then check numeric fields (AGE, TRTDURD after casting) for outliers. I'll also check for missing value patterns represented as strings like '..MISSING..' which appear in the ranges.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_rows, COUNT(CASE WHEN RFSTDTC = '..MISSING..' THEN 1 END) as rfstdtc_missing, COUNT(CASE WHEN RFENDTC = '..MISSING..' THEN 1 END) as rfendtc_missing, COUNT(CASE WHEN RFXSTDTC = '..MISSING..' THEN 1 END) as rfxstdtc_missing, COUNT(CASE WHEN RFXENDTC = '..MISSING..' THEN 1 END) as rfxendtc_missing, COUNT(CASE WHEN DTHDTC = '..MISSING..' THEN 1 END) as dthdtc_missing, COUNT(CASE WHEN TRTDURD = '..MISSING..' THEN 1 END) as trtdurd_missing, COUNT(CASE WHEN DMDY = '..MISSING..' THEN 1 END) as dmdy_missing, MIN(AGE) as min_age, MAX(AGE) as max_age, AVG(AGE) as avg_age FROM raw.adsl",
        "purpose": "Check for missing value patterns encoded as strings and basic age statistics",
        "source": "raw"
    }
}