I need to identify the main categorical columns in this dataset and find their most common values with frequencies. Let me start by examining the column metadata to identify categorical columns across the key tables. ```json { "action": "tool_call", "reasoning": "I need to identify categorical columns across the dataset. The analytics.column_overview metadata will show me columns with their semantic roles and rough kinds, which will help identify categorical fields. I'll focus on the largest tables first (columns_catalog, job_logs, llm_calls, column_tags, tag_lineage_edges) as mentioned in the evidence.", "tool": "query_sql", "args": { "sql": "SELECT table_name, column_name, phys_type, semantic_role, rough_kind, distinct_count, row_count FROM analytics.column_overview WHERE table_name IN ('columns_catalog', 'job_logs', 'llm_calls', 'column_tags', 'tag_lineage_edges', 'tables_catalog', 'batches', 'analysis_runs') AND rough_kind IN ('categorical', 'enum', 'flag') ORDER BY row_count DESC, distinct_count ASC", "purpose": "Identify main categorical columns across key tables to determine which ones to profile for most common values", "source": "raw" } } ```