```json { "action": "tool_call", "reasoning": "I have the column metadata from inspect_columns and DESCRIBE, showing phys_type, semantic_role, and data_type. Now I need to query the actual table to get complete statistics (null counts, distinct counts, min/max values) for all columns in one pass, which will let me definitively classify each column by its actual data characteristics rather than just metadata labels.", "tool": "query_sql", "args": { "sql": "SELECT COUNT(*) as total_rows, COUNT(DISTINCT Name) as distinct_names, COUNT(DISTINCT Status) as distinct_status, COUNT(DISTINCT Certainty) as distinct_certainty, COUNT(DISTINCT \"Single cluster?\") as distinct_single_cluster, COUNT(DISTINCT \"Chip type (primary)\") as distinct_chip_type_primary, COUNT(DISTINCT \"Chip type (secondary)\") as distinct_chip_type_secondary, COUNT(DISTINCT Country) as distinct_countries, COUNT(DISTINCT Owner) as distinct_owners, COUNT(DISTINCT Sector) as distinct_sectors, COUNT(DISTINCT \"GPU Supplier (primary)\") as distinct_gpu_supplier_primary, COUNT(DISTINCT \"GPU supplier (secondary)\") as distinct_gpu_supplier_secondary, COUNT(DISTINCT \"Include in Standard Analysis\") as distinct_include_analysis, COUNT(DISTINCT Exclude) as distinct_exclude, COUNT(DISTINCT Noteworthy) as distinct_noteworthy, COUNT(DISTINCT \"Builds Upon\") as distinct_builds_upon, COUNT(DISTINCT \"Superseded by\") as distinct_superseded_by, COUNT(\"First Operational Date\") as non_null_dates, COUNT(\"Max OP/s (log)\") as non_null_max_ops, COUNT(\"H100 equivalents\") as non_null_h100, COUNT(\"Chip quantity (primary)\") as non_null_chip_qty_primary, COUNT(\"Power Capacity (MW)\") as non_null_power, COUNT(\"Hardware Cost\") as non_null_hw_cost FROM raw.ai_supercomputers", "purpose": "Get cardinality and null counts for key columns to classify them as identifiers (high cardinality), categorical dimensions (low cardinality), or numeric metrics (continuous values with nulls)", "source": "raw" } } ```