```json { "action": "tool_call", "reasoning": "The evidence shows the dataset has a complex structure with multiple sections (daily summaries, trends, outcomes, escalation reasons) mixed together. I need to identify which rows represent actual categorical data vs. section headers/totals. From step-5, I can see the Day column contains day-of-week values (Thu, Fri, Sat, etc.), dates (23-Jun, 24-Jun, etc.), and section headers. I should query to get the most common values for the main categorical columns, filtering out headers and totals. Let me start by examining what the actual categorical values are across the key columns, excluding rows that are clearly headers or aggregates.", "tool": "query_sql", "args": { "sql": "SELECT Day, COUNT(*) as frequency FROM raw.callyeah_analytics_2026_07_23 WHERE Day NOT IN ('TOTAL', '', 'Date', '=== DAILY TREND — LAST 30 DAYS ===', '=== CALL OUTCOMES (30-DAY PERIOD) ===', '=== ESCALATION REASONS (30-DAY PERIOD) ===') AND Day NOT LIKE 'CY-%' AND Day NOT LIKE '%===%' GROUP BY Day ORDER BY frequency DESC LIMIT 20", "purpose": "Get the most common Day values excluding headers and totals", "source": "raw" } } ```