{
    "action": "tool_call",
    "reasoning": "The question asks about natural segmentation opportunities across the dataset. I've seen the overview showing beer_ratings dominates (85% of rows), and language distribution showing English is 93.5% of ratings. To identify meaningful segments, I should examine: 1) temporal patterns (ratings span 2000-2025), 2) product characteristics (style_id, alcohol_pct, seasonal/one-off flags), 3) user behavior patterns, 4) geographic distribution. Let me start by checking if there are clear temporal segments in the rating activity.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT DATE_TRUNC('year', created_at) as year, COUNT(*) as rating_count, COUNT(DISTINCT user_id) as active_users, COUNT(DISTINCT beer_id) as beers_rated, ROUND(AVG(total_score), 2) as avg_score FROM raw.beer_ratings WHERE created_at IS NOT NULL GROUP BY DATE_TRUNC('year', created_at) ORDER BY year",
        "purpose": "Examine temporal distribution of ratings to identify natural time-based segments (growth phases, activity peaks)",
        "source": "raw"
    }
}