{
    "action": "tool_call",
    "reasoning": "The quality report shows a perfect 100% score with zero missing values, but that only covers completeness. To find actual data quality issues (outliers, impossible values, inconsistent formats, mixed units), I need to examine the data itself. I'll start by profiling key columns across both tables to look for: (1) temporal inconsistencies (dates out of order, impossible dates), (2) numeric outliers in measures like AGE, (3) format inconsistencies in date/time fields, (4) logical impossibilities (end dates before start dates, negative durations). I'll write one comprehensive query to check multiple quality dimensions at once.",
    "tool": "query_sql",
    "source": "raw",
    "purpose": "Profile data quality issues: check for temporal inconsistencies (end before start), impossible ages, date format variations, negative durations, and logical contradictions across key columns in adsl",
    "sql": "WITH date_checks AS (\n  SELECT\n    COUNT(*) as total_subjects,\n    COUNT(CASE WHEN RFENDTC < RFSTDTC AND RFENDTC != '..MISSING..' AND RFSTDTC != '..MISSING..' THEN 1 END) as ref_end_before_start,\n    COUNT(CASE WHEN RFXENDTC < RFXSTDTC AND RFXENDTC != '..MISSING..' AND RFXSTDTC != '..MISSING..' THEN 1 END) as rfx_end_before_start,\n    COUNT(CASE WHEN TRTEDTM < TRTSDTM AND TRTEDTM != '..MISSING..' AND TRTSDTM != '..MISSING..' THEN 1 END) as trt_end_before_start,\n    COUNT(CASE WHEN AGE < 18 THEN 1 END) as age_under_18,\n    COUNT(CASE WHEN AGE > 120 THEN 1 END) as age_over_120,\n    COUNT(CASE WHEN DMDY NOT LIKE '..MISSING..' AND CAST(DMDY AS INTEGER) < -365 THEN 1 END) as dmdy_extreme_negative,\n    COUNT(CASE WHEN TRTDURD < 0 THEN 1 END) as negative_duration,\n    COUNT(CASE WHEN DTHFL = 'Y' AND DTHDTC = '..MISSING..' THEN 1 END) as death_flag_no_date,\n    COUNT(CASE WHEN DTHFL != 'Y' AND DTHDTC != '..MISSING..' THEN 1 END) as death_date_no_flag\n  FROM raw.adsl\n),\nage_stats AS (\n  SELECT\n    MIN(AGE) as min_age,\n    MAX(AGE) as max_age,\n    AVG(AGE) as mean_age,\n    MEDIAN(AGE) as median_age,\n    STDDEV(AGE) as stddev_age,\n    PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY AGE) as q1_age,\n    PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY AGE) as q3_age\n  FROM raw.adsl\n)\nSELECT * FROM date_checks, age_stats"
}