{
    "action": "tool_call",
    "reasoning": "I've already profiled beer_ratings and beers for basic range validation. Now I need to check the other major tables for quality issues. Let me start with users, brewers, and places to look for outliers, impossible values, and format inconsistencies. I'll profile key numeric and temporal columns in one query per table.",
    "tool": "query_sql",
    "purpose": "Profile users table for data quality issues - check temporal consistency, rating counts, and numeric ranges",
    "sql": "SELECT COUNT(*) as total_users, COUNT(DISTINCT user_id) as distinct_ids, COUNT(*) - COUNT(DISTINCT user_id) as duplicate_ids, COUNT(beer_first_rating) as has_first_rating, MIN(beer_first_rating) as min_first_rating, MAX(beer_first_rating) as max_first_rating, COUNT(beer_last_rating) as has_last_rating, MIN(beer_last_rating) as min_last_rating, MAX(beer_last_rating) as max_last_rating, COUNT(CASE WHEN beer_last_rating < beer_first_rating THEN 1 END) as last_before_first, COUNT(place_first_rating) as has_place_first, MIN(place_first_rating) as min_place_first, MAX(place_first_rating) as max_place_first, COUNT(place_last_rating) as has_place_last, MIN(place_last_rating) as min_place_last, MAX(place_last_rating) as max_place_last, COUNT(CASE WHEN place_last_rating < place_first_rating THEN 1 END) as place_last_before_first, COUNT(beer_ratings_count) as has_beer_count, MIN(beer_ratings_count) as min_beer_count, MAX(beer_ratings_count) as max_beer_count, COUNT(place_ratings_count) as has_place_count, MIN(place_ratings_count) as min_place_count, MAX(place_ratings_count) as max_place_count FROM raw.users"
}