{
    "action": "tool_call",
    "reasoning": "The evidence shows an intriguing pattern: beers with MORE ratings actually have HIGHER average scores (1000+ ratings: 3.53 avg vs 1 rating: 3.41 avg), which contradicts the typical 'regression to the mean' expectation. And the quality tier analysis shows top-rated beers receive 55% more ratings (446 vs 287) despite being active for LESS time (3925 vs 6120 days). This suggests a 'quality snowball' effect. But I need to verify this isn't just survivorship bias - let me check if low-rated beers are being systematically removed or if highly-rated beers truly attract disproportionate attention even when controlling for age.",
    "tool": "query_sql",
    "args": {
        "sql": "WITH beer_metrics AS (SELECT b.beer_id, b.created_at, COUNT(r.rating_id) AS rating_count, AVG(r.total_score) AS avg_score, DATEDIFF('day', b.created_at, MAX(r.created_at)) AS days_active FROM raw.beers b LEFT JOIN raw.beer_ratings r ON b.beer_id = r.beer_id WHERE b.created_at IS NOT NULL AND r.total_score IS NOT NULL GROUP BY b.beer_id, b.created_at HAVING rating_count >= 100), age_cohorts AS (SELECT CASE WHEN days_active < 1825 THEN '<5 years' WHEN days_active < 3650 THEN '5-10 years' ELSE '10+ years' END AS age_group, CASE WHEN avg_score < 3.0 THEN 'Poor (<3.0)' WHEN avg_score < 3.5 THEN 'Average (3.0-3.5)' WHEN avg_score < 4.0 THEN 'Good (3.5-4.0)' ELSE 'Excellent (4.0+)' END AS quality_tier, beer_id, rating_count, avg_score FROM beer_metrics) SELECT age_group, quality_tier, COUNT(*) AS beer_count, ROUND(AVG(rating_count), 0) AS avg_ratings, ROUND(MEDIAN(rating_count), 0) AS median_ratings, ROUND(MAX(rating_count), 0) AS max_ratings FROM age_cohorts GROUP BY age_group, quality_tier ORDER BY age_group, quality_tier",
        "purpose": "Test if the 'quality attracts attention' pattern holds when controlling for beer age - do excellent beers get more ratings than poor beers even within the same age cohort?",
        "source": "raw"
    }
}