```json { "action": "tool_call", "reasoning": "The inspect_columns result shows all columns are VARCHAR type, but the question asks for numeric column distributions. The range values suggest some columns contain numeric data stored as text (e.g., 'Total Calls', 'AI Handled', 'Human Handled', 'Voicemail', 'AI Resolution %', 'Human %'). I need to query the actual data to identify which columns contain numeric values and compute their distributions. I'll write a single SQL query that attempts to cast the likely numeric columns and compute min, max, mean, median, and stddev for each.", "tool": "query_sql", "args": { "sql": "SELECT COUNT(*) as row_count, MIN(TRY_CAST(\"Total Calls\" AS DOUBLE)) as total_calls_min, MAX(TRY_CAST(\"Total Calls\" AS DOUBLE)) as total_calls_max, AVG(TRY_CAST(\"Total Calls\" AS DOUBLE)) as total_calls_mean, MEDIAN(TRY_CAST(\"Total Calls\" AS DOUBLE)) as total_calls_median, STDDEV(TRY_CAST(\"Total Calls\" AS DOUBLE)) as total_calls_stddev, MIN(TRY_CAST(\"AI Handled\" AS DOUBLE)) as ai_handled_min, MAX(TRY_CAST(\"AI Handled\" AS DOUBLE)) as ai_handled_max, AVG(TRY_CAST(\"AI Handled\" AS DOUBLE)) as ai_handled_mean, MEDIAN(TRY_CAST(\"AI Handled\" AS DOUBLE)) as ai_handled_median, STDDEV(TRY_CAST(\"AI Handled\" AS DOUBLE)) as ai_handled_stddev, MIN(TRY_CAST(\"Human Handled\" AS DOUBLE)) as human_handled_min, MAX(TRY_CAST(\"Human Handled\" AS DOUBLE)) as human_handled_max, AVG(TRY_CAST(\"Human Handled\" AS DOUBLE)) as human_handled_mean, MEDIAN(TRY_CAST(\"Human Handled\" AS DOUBLE)) as human_handled_median, STDDEV(TRY_CAST(\"Human Handled\" AS DOUBLE)) as human_handled_stddev, MIN(TRY_CAST(\"Voicemail\" AS DOUBLE)) as voicemail_min, MAX(TRY_CAST(\"Voicemail\" AS DOUBLE)) as voicemail_max, AVG(TRY_CAST(\"Voicemail\" AS DOUBLE)) as voicemail_mean, MEDIAN(TRY_CAST(\"Voicemail\" AS DOUBLE)) as voicemail_median, STDDEV(TRY_CAST(\"Voicemail\" AS DOUBLE)) as voicemail_stddev, MIN(TRY_CAST(REPLACE(\"AI Resolution %\", '%', '') AS DOUBLE)) as ai_resolution_pct_min, MAX(TRY_CAST(REPLACE(\"AI Resolution %\", '%', '') AS DOUBLE)) as ai_resolution_pct_max, AVG(TRY_CAST(REPLACE(\"AI Resolution %\", '%', '') AS DOUBLE)) as ai_resolution_pct_mean, MEDIAN(TRY_CAST(REPLACE(\"AI Resolution %\", '%', '') AS DOUBLE)) as ai_resolution_pct_median, STDDEV(TRY_CAST(REPLACE(\"AI Resolution %\", '%', '') AS DOUBLE)) as ai_resolution_pct_stddev, MIN(TRY_CAST(REPLACE(\"Human %\", '%', '') AS DOUBLE)) as human_pct_min, MAX(TRY_CAST(REPLACE(\"Human %\", '%', '') AS DOUBLE)) as human_pct_max, AVG(TRY_CAST(REPLACE(\"Human %\", '%', '') AS DOUBLE)) as human_pct_mean, MEDIAN(TRY_CAST(REPLACE(\"Human %\", '%', '') AS DOUBLE)) as human_pct_median, STDDEV(TRY_CAST(REPLACE(\"Human %\", '%', '') AS DOUBLE)) as human_pct_stddev FROM raw.callyeah_analytics_2026_07_23", "purpose": "Compute distribution statistics (min, max, mean, median, stddev) for all numeric columns in the dataset", "source": "raw" } } ```