```json { "action": "tool_call", "reasoning": "I have daily engagement data showing deal counts and total values from October 2016 through at least mid-December 2016. The results show 50 rows but are truncated. To identify ALL notable spikes, dips, and breaks across the full time range, I need to get the complete dataset. I'll query for the full time series and add statistical context (moving averages or percentiles) to objectively identify what constitutes 'notable' deviations.", "tool": "query_sql", "args": { "sql": "WITH daily_stats AS (SELECT CAST(engage_date AS DATE) AS day, COUNT(*) AS deal_count, SUM(close_value) AS total_value FROM raw.b2b_sales_funnel_pipeline GROUP BY CAST(engage_date AS DATE)), stats_with_avg AS (SELECT day, deal_count, total_value, AVG(deal_count) OVER (ORDER BY day ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS deal_count_7day_avg, AVG(total_value) OVER (ORDER BY day ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS value_7day_avg FROM daily_stats) SELECT day, deal_count, total_value, ROUND(deal_count_7day_avg, 1) AS deal_count_7day_avg, ROUND(value_7day_avg, 0) AS value_7day_avg FROM stats_with_avg ORDER BY day", "purpose": "Get complete daily time series with 7-day moving averages to identify spikes, dips, and breaks in deal activity", "source": "raw" } } ```