{
    "action": "tool_call",
    "reasoning": "I've already identified the main categorical columns in step-7 (columns with rough_kind='categorical' or semantic_role='classifier'/'descriptive' that are categorical). Step-9 gave me the top 10 values for several key categorical columns (job_logs.phase, column_tags.tag, job_logs.dataset_id, job_logs.function_name, job_logs.job_id). However, I need to get a more complete picture across ALL the main categorical columns. Let me query for the top values across the most important categorical columns that weren't fully covered in step-9, particularly focusing on columns with high distinct counts that indicate they're actively used dimensions.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT 'llm_calls.purpose' as column_name, purpose as value, COUNT(*) as frequency FROM raw.llm_calls WHERE purpose IS NOT NULL GROUP BY purpose ORDER BY frequency DESC LIMIT 10 UNION ALL SELECT 'batches.status', status, COUNT(*) FROM raw.batches WHERE status IS NOT NULL GROUP BY status ORDER BY COUNT(*) DESC LIMIT 10 UNION ALL SELECT 'batches.nettle_version', nettle_version, COUNT(*) FROM raw.batches WHERE nettle_version IS NOT NULL GROUP BY nettle_version ORDER BY COUNT(*) DESC LIMIT 10 UNION ALL SELECT 'job_logs.source_module', source_module, COUNT(*) FROM raw.job_logs WHERE source_module IS NOT NULL GROUP BY source_module ORDER BY COUNT(*) DESC LIMIT 10 UNION ALL SELECT 'job_logs.file_path', file_path, COUNT(*) FROM raw.job_logs WHERE file_path IS NOT NULL GROUP BY file_path ORDER BY COUNT(*) DESC LIMIT 10",
        "purpose": "Get top values and frequencies for additional key categorical columns not fully covered in previous results",
        "source": "raw"
    }
}