I need to compute distribution statistics for all numeric columns in the dataset. From the evidence, I can see that step-5 gave me the columns of `beer_ratings`, which has several numeric columns (aroma, flavor, mouthfeel, appearance, overall, total_score, description_score). However, I need to check all tables in the dataset for numeric columns and compute their distributions. Let me start by identifying all numeric columns across all tables, then compute their statistics in one efficient query. ```json { "action": "tool_call", "reasoning": "I need to identify all numeric columns across all tables in the dataset. The metadata table analytics.column_overview contains phys_type which will tell me which columns are numeric (BIGINT, DOUBLE, DECIMAL, etc.). I'll query this to get a complete list of numeric columns.", "tool": "query_sql", "args": { "sql": "SELECT table_name, column_name, phys_type FROM analytics.column_overview WHERE phys_type IN ('BIGINT', 'DOUBLE', 'DECIMAL', 'INTEGER', 'FLOAT', 'NUMERIC') ORDER BY table_name, column_name", "purpose": "Identify all numeric columns across all tables to compute distribution statistics", "source": "raw" } } ```