```json { "action": "tool_call", "reasoning": "The evidence shows the dataset has mixed content - some rows are daily summaries (Thu/Fri/Sat etc.), some are date-based (23-Jun, 24-Jun etc.), and some are section headers or call detail records. The columns contain different types of data depending on the row type. I need to identify which rows represent actual call records vs summaries/headers, then analyze the categorical values. Let me query to understand the row structure better and identify the main categorical columns with their value distributions.", "tool": "query_sql", "args": { "sql": "SELECT Day, COUNT(*) as row_count, COUNT(DISTINCT \"Total Calls\") as distinct_total_calls, COUNT(DISTINCT \"AI Handled\") as distinct_ai_handled, COUNT(DISTINCT \"Human Handled\") as distinct_human_handled, COUNT(DISTINCT Voicemail) as distinct_voicemail FROM raw.callyeah_analytics_2026_07_23 WHERE Day NOT IN ('', 'TOTAL', '=== DAILY TREND — LAST 30 DAYS ===', '=== CALL OUTCOMES (30-DAY PERIOD) ===', '=== ESCALATION REASONS (30-DAY PERIOD) ===') AND \"Total Calls\" NOT IN ('', 'Count', 'Total Calls') GROUP BY Day ORDER BY row_count DESC LIMIT 20", "purpose": "Identify the structure of rows - which Day values represent summaries vs individual records, to understand what the categorical columns actually contain" } } ```