I'll analyze the temporal patterns in this dataset to identify notable spikes, dips, or breaks. ```json { "action": "tool_call", "reasoning": "The dataset has multiple temporal axes. The largest table is beer_ratings with created_at spanning 2000-2025. I'll start by examining the overall temporal distribution of beer ratings to identify spikes, dips, and breaks in activity.", "tool": "query_sql", "args": { "sql": "SELECT DATE_TRUNC('month', created_at) as month, COUNT(*) as rating_count FROM raw.beer_ratings WHERE created_at IS NOT NULL GROUP BY month ORDER BY month", "purpose": "Get monthly beer rating counts to identify temporal patterns, spikes, and dips over the full 25-year span" } } ```