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. I'll query the metadata to find columns with numeric physical types (BIGINT, DOUBLE, DECIMAL, INTEGER, etc.).", "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') AND table_name LIKE 'raw.%' ORDER BY table_name, column_name", "purpose": "Find all numeric columns across all raw tables to compute distribution statistics" } } ```