I need to identify the main categorical columns and find their most common values with frequencies. From the evidence, I can see that step-4 gave me a comprehensive list of columns with their types and semantic roles. Let me analyze the categorical columns and get their value distributions. From step-4, I can see many categorical columns across different tables. Let me focus on the most significant ones and get their value distributions systematically. ```json { "action": "tool_call", "reasoning": "I have categorical columns identified from step-4. Now I need to get the most common values for the main categorical columns across key tables. I'll start with the most important tables like batches, analysis_runs, datasets, and other core tables. I'll query multiple categorical columns in one go to be efficient.", "tool": "query_sql", "args": { "sql": "SELECT 'batches.nettle_version' AS column_name, nettle_version AS value, COUNT(*) AS frequency FROM raw.batches WHERE nettle_version IS NOT NULL GROUP BY nettle_version ORDER BY frequency DESC LIMIT 10", "purpose": "Get top values for batches.nettle_version", "source": "raw" } } ```