I need to compute distribution statistics (min, max, mean, median, spread) for all numeric columns across all tables in the dataset. Let me write a comprehensive query that profiles all numeric columns at once. ```json { "action": "tool_call", "reasoning": "I need to compute distribution statistics for all numeric columns. From the evidence, I can see many numeric columns across different tables (BIGINT and DOUBLE types). I'll write one SQL query that computes min, max, mean, median, and standard deviation (as a measure of spread) for all numeric columns by dynamically generating aggregations. Since there are many columns, I'll use a UNION approach to profile each numeric column.", "tool": "query_sql", "args": { "sql": "WITH numeric_columns AS (SELECT table_name, column_name, phys_type FROM meta.columns WHERE phys_type IN ('BIGINT', 'DOUBLE', 'DECIMAL', 'INTEGER', 'FLOAT', 'NUMERIC')) SELECT table_name, column_name, phys_type, COUNT(*) as total_rows, COUNT(CASE WHEN column_name = 'calls' THEN 1 END) as non_null_count FROM numeric_columns CROSS JOIN raw.analysis_run_llm_usage GROUP BY table_name, column_name, phys_type LIMIT 50", "purpose": "First, identify all numeric columns across all tables to then compute their distributions", "source": "raw" } } ```