{
    "action": "tool_call",
    "reasoning": "I have evidence of missing value patterns and basic ranges, but need to check for outliers and impossible values in the numeric measures. The previous attempts failed due to type casting issues with VARCHAR columns containing '..MISSING..' markers. I need to examine the actual distribution of numeric values (AGE, TRTDURD after filtering out missing markers) and check for outliers. I'll also examine the AE table for temporal inconsistencies and severity/outcome combinations that might be logically impossible.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_subjects, COUNT(CASE WHEN AGE < 18 THEN 1 END) as age_under_18, COUNT(CASE WHEN AGE > 110 THEN 1 END) as age_over_110, PERCENTILE_CONT(0.01) WITHIN GROUP (ORDER BY AGE) as age_p1, PERCENTILE_CONT(0.99) WITHIN GROUP (ORDER BY AGE) as age_p99, COUNT(CASE WHEN TRTDURD != '..MISSING..' AND CAST(TRTDURD AS INTEGER) > 365 THEN 1 END) as duration_over_year, COUNT(CASE WHEN TRTDURD != '..MISSING..' AND CAST(TRTDURD AS INTEGER) < 1 THEN 1 END) as duration_under_1_day, PERCENTILE_CONT(0.01) WITHIN GROUP (ORDER BY CASE WHEN TRTDURD != '..MISSING..' THEN CAST(TRTDURD AS INTEGER) END) as duration_p1, PERCENTILE_CONT(0.99) WITHIN GROUP (ORDER BY CASE WHEN TRTDURD != '..MISSING..' THEN CAST(TRTDURD AS INTEGER) END) as duration_p99 FROM raw.adsl",
        "purpose": "Check for outliers in age and treatment duration using percentiles and extreme value counts",
        "source": "raw"
    }
}