```json { "action": "tool_call", "reasoning": "The dataset card shows multiple temporal axes across both tables. The most relevant for analyzing spikes/dips/breaks would be the adverse event occurrence dates (raw.ae.AESTDTC, AEDTC, AEENDTC) and the subject treatment dates (raw.adsl.RFSTDTC, RFENDTC, etc.). I'll start by examining the distribution of adverse events over time since that's the primary fact table. I'll query the count of adverse events by month to identify any notable patterns.", "tool": "query_sql", "args": { "sql": "SELECT DATE_TRUNC('month', CAST(AESTDTC AS DATE)) as month, COUNT(*) as event_count FROM raw.ae WHERE AESTDTC IS NOT NULL AND AESTDTC != '' AND LENGTH(AESTDTC) >= 10 GROUP BY DATE_TRUNC('month', CAST(AESTDTC AS DATE)) ORDER BY month", "purpose": "Analyze adverse event counts by month to identify spikes, dips, or breaks in the time series", "source": "raw" } } ```