{
    "action": "tool_call",
    "reasoning": "The quality report highlights structural issues (missing data, PII) but doesn't detail row-level anomalies like outliers, impossible values, or format inconsistencies. I need to profile key numeric and date columns across the main tables to find: (1) temporal outliers (dates in the future or impossibly old), (2) impossible numeric values (negative counts, ratings outside valid ranges), (3) format inconsistencies in text fields. I'll start with a comprehensive profile of critical columns in beer_ratings, beers, and users — the three largest tables with the most analytical value.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) AS total_ratings, COUNT(DISTINCT user_id) AS distinct_users, COUNT(DISTINCT beer_id) AS distinct_beers, MIN(created_at) AS earliest_rating, MAX(created_at) AS latest_rating, MIN(updated_at) AS earliest_update, MAX(updated_at) AS latest_update, MIN(rating) AS min_rating, MAX(rating) AS max_rating, AVG(rating) AS avg_rating, MEDIAN(rating) AS median_rating, STDDEV(rating) AS stddev_rating, COUNT(CASE WHEN rating < 0 THEN 1 END) AS negative_ratings, COUNT(CASE WHEN rating > 5 THEN 1 END) AS above_max_ratings, COUNT(CASE WHEN created_at > CURRENT_TIMESTAMP THEN 1 END) AS future_created, COUNT(CASE WHEN updated_at > CURRENT_TIMESTAMP THEN 1 END) AS future_updated, COUNT(CASE WHEN created_at < '1990-01-01' THEN 1 END) AS pre_1990_created, COUNT(CASE WHEN updated_at < created_at THEN 1 END) AS update_before_create FROM raw.beer_ratings",
        "purpose": "Profile beer_ratings for temporal outliers (future dates, pre-1990 dates, updates before creates) and impossible rating values (negative, >5)",
        "source": "raw"
    }
}