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 characteristics. Let me analyze the categorical columns that appear to be most significant and query their value distributions. From the evidence, I've already obtained some value distributions: - batches.tenant_id (step-5): "default" appears 304 times - batches.status (step-6, step-11): "ready" (153), "completed" (132), "running" (19) - batches.nettle_version (step-12): "1.0.0" (153), "expansion" (151) - analysis_runs.code_version (step-7): empty value appears 116 times Let me get distributions for other important categorical columns I can see in the data. ```json { "action": "tool_call", "reasoning": "I need to get value distributions for additional important categorical columns. From step-4, I can see several key categorical columns like llm_calls.model, llm_calls.provider, llm_calls.status, nia_messages.role, column_tags.tag, and others. Let me query the most common values for llm_calls which appears to be a significant table.", "tool": "query_sql", "args": { "sql": "SELECT model as value, COUNT(*) as frequency FROM raw.llm_calls WHERE model IS NOT NULL GROUP BY model ORDER BY frequency DESC LIMIT 10", "purpose": "Get top 10 most common LLM models used" } } ```