I need to identify the main categorical columns across the key tables and find their most common values with frequencies. Let me start by examining the column metadata to identify categorical columns. ```json { "action": "tool_call", "reasoning": "I need to identify the main categorical columns in the dataset. The overview shows beer_ratings, beers, users, place_ratings, and brewers as key tables. I'll query the column metadata to find categorical columns (VARCHAR/TEXT types with semantic roles like 'category', 'label', or 'identifier').", "tool": "query_sql", "args": { "sql": "SELECT table_name, column_name, phys_type, semantic_role, rough_kind, distinct_count FROM analytics.column_overview WHERE table_name IN ('beer_ratings', 'beers', 'users', 'place_ratings', 'brewers', 'places') AND rough_kind IN ('categorical', 'label', 'identifier') AND distinct_count < 1000 ORDER BY table_name, distinct_count DESC", "purpose": "Identify main categorical columns with manageable cardinality for frequency analysis" } } ```