{
    "action": "tool_call",
    "reasoning": "I need to examine the actual distribution of rating component values to identify outliers and impossible values. The inspect_columns output shows ranges, but I need to see the actual distributions and check for values that violate the stated scales (aroma/flavor 0-10, mouthfeel/appearance 0-5, overall 0-20). I'll also check total_score against its components to find inconsistencies.",
    "tool": "query_sql",
    "purpose": "Profile rating component distributions and identify outliers or impossible values",
    "args": {
        "sql": "SELECT COUNT(*) as total_ratings, COUNT(DISTINCT aroma) as distinct_aroma, COUNT(DISTINCT flavor) as distinct_flavor, COUNT(DISTINCT mouthfeel) as distinct_mouthfeel, COUNT(DISTINCT appearance) as distinct_appearance, COUNT(DISTINCT overall) as distinct_overall, MIN(aroma) as min_aroma, MAX(aroma) as max_aroma, MIN(flavor) as min_flavor, MAX(flavor) as max_flavor, MIN(mouthfeel) as min_mouthfeel, MAX(mouthfeel) as max_mouthfeel, MIN(appearance) as min_appearance, MAX(appearance) as max_appearance, MIN(overall) as min_overall, MAX(overall) as max_overall, MIN(total_score) as min_total, MAX(total_score) as max_total, COUNT(CASE WHEN total_score < 0 OR total_score > 5 THEN 1 END) as total_score_out_of_range, COUNT(CASE WHEN aroma > 10 THEN 1 END) as aroma_violations, COUNT(CASE WHEN flavor > 10 THEN 1 END) as flavor_violations, COUNT(CASE WHEN mouthfeel > 5 THEN 1 END) as mouthfeel_violations, COUNT(CASE WHEN appearance > 5 THEN 1 END) as appearance_violations, COUNT(CASE WHEN overall > 20 THEN 1 END) as overall_violations FROM raw.beer_ratings",
        "source": "raw"
    }
}