{
    "action": "tool_call",
    "reasoning": "I need to systematically check for data quality issues across the dataset. Starting with the beer_ratings table since it's the largest (14.3M rows) and contains multiple measure columns. I'll check for: (1) impossible values in the rating components (aroma, flavor, mouthfeel, appearance, overall should have valid ranges), (2) inconsistencies between component scores and total_score, (3) outliers in description_score which has a suspicious range of -10495 to 500.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_ratings, COUNT(CASE WHEN aroma < 0 OR aroma > 10 THEN 1 END) as invalid_aroma, COUNT(CASE WHEN flavor < 0 OR flavor > 10 THEN 1 END) as invalid_flavor, COUNT(CASE WHEN mouthfeel < 0 OR mouthfeel > 5 THEN 1 END) as invalid_mouthfeel, COUNT(CASE WHEN appearance < 0 OR appearance > 5 THEN 1 END) as invalid_appearance, COUNT(CASE WHEN overall < 0 OR overall > 20 THEN 1 END) as invalid_overall, COUNT(CASE WHEN total_score < 0 OR total_score > 5 THEN 1 END) as invalid_total_score, COUNT(CASE WHEN description_score < -100 OR description_score > 100 THEN 1 END) as suspicious_description_score, MIN(description_score) as min_desc_score, MAX(description_score) as max_desc_score, MEDIAN(description_score) as median_desc_score FROM raw.beer_ratings",
        "purpose": "Check for out-of-range values in beer_ratings measure columns",
        "source": "raw"
    }
}