I need to count distinct values for all categorical columns. From the inspect_columns results, I can see several categorical columns (dimensions and identifiers), but I should query all of them systematically to get exact counts. Let me write a single SQL query that profiles all categorical columns at once: ```json { "action": "tool_call", "reasoning": "I need to count distinct values for all categorical columns. From the inspect_columns output, I can see the approximate distinct counts, but I should get exact counts for all categorical/dimension columns in one query. I'll select all VARCHAR and BOOLEAN columns and count their distinct values.", "tool": "query_sql", "args": { "sql": "SELECT COUNT(DISTINCT Name) as Name_distinct, COUNT(DISTINCT Status) as Status_distinct, COUNT(DISTINCT Certainty) as Certainty_distinct, COUNT(DISTINCT \"Single cluster?\") as Single_cluster_distinct, COUNT(DISTINCT \"Chip type (primary)\") as Chip_type_primary_distinct, COUNT(DISTINCT Country) as Country_distinct, COUNT(DISTINCT Owner) as Owner_distinct, COUNT(DISTINCT Sector) as Sector_distinct, COUNT(DISTINCT Location) as Location_distinct, COUNT(DISTINCT Users) as Users_distinct, COUNT(DISTINCT \"Hardware note\") as Hardware_note_distinct, COUNT(DISTINCT \"First Operational Date Note\") as First_Operational_Date_Note_distinct, COUNT(DISTINCT \"Certainty Note\") as Certainty_Note_distinct, COUNT(DISTINCT \"Builds Upon\") as Builds_Upon_distinct, COUNT(DISTINCT \"Superseded by\") as Superseded_by_distinct, COUNT(DISTINCT \"Possible Duplicate\") as Possible_Duplicate_distinct, COUNT(DISTINCT \"Possible Duplicate Of\") as Possible_Duplicate_Of_distinct, COUNT(DISTINCT \"Chip type (secondary)\") as Chip_type_secondary_distinct, COUNT(DISTINCT \"GPU Supplier (primary)\") as GPU_Supplier_primary_distinct FROM raw.ai_supercomputers", "purpose": "Count distinct values for all categorical columns in the dataset", "source": "raw" } } ```