{
    "action": "tool_call",
    "reasoning": "I have strong evidence for temporal, geographic, style, and product characteristic segmentation. To complete the picture, I should profile alcohol content distribution (a key product attribute) and user engagement tiers (to understand the reviewer base segmentation). Let me get alcohol_pct distribution first.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT CASE WHEN alcohol_pct IS NULL THEN 'Unknown' WHEN alcohol_pct = 0 THEN 'Non-alcoholic (0%)' WHEN alcohol_pct < 4 THEN 'Low (0-4%)' WHEN alcohol_pct < 6 THEN 'Standard (4-6%)' WHEN alcohol_pct < 8 THEN 'Strong (6-8%)' WHEN alcohol_pct < 10 THEN 'Very Strong (8-10%)' ELSE 'Extreme (10%+)' END AS abv_segment, COUNT(*) AS beer_count, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS pct_of_beers, ROUND(MIN(alcohol_pct), 1) AS min_abv, ROUND(MAX(alcohol_pct), 1) AS max_abv FROM raw.beers GROUP BY 1 ORDER BY MIN(alcohol_pct) NULLS FIRST",
        "purpose": "Profile alcohol content distribution to identify natural ABV-based product segments",
        "source": "raw"
    }
}