{
    "action": "tool_call",
    "reasoning": "I need to examine the distribution of job titles, industries, and experience levels to identify natural groupings. Since company_size is artificially balanced, I'll query the raw table to see which dimensions show meaningful clustering patterns. I'll get counts and percentages for these key categorical variables in a single query.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT 'job_title' as dimension, job_title as category, COUNT(*) as job_count, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) as pct FROM raw.ai_job_dataset GROUP BY job_title UNION ALL SELECT 'industry', industry, COUNT(*), ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) FROM raw.ai_job_dataset GROUP BY industry UNION ALL SELECT 'experience_level', experience_level, COUNT(*), ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) FROM raw.ai_job_dataset GROUP BY experience_level UNION ALL SELECT 'remote_ratio', CAST(remote_ratio AS VARCHAR), COUNT(*), ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) FROM raw.ai_job_dataset GROUP BY remote_ratio ORDER BY dimension, job_count DESC",
        "purpose": "Identify natural segmentation dimensions by examining the distribution of job titles, industries, experience levels, and remote work ratios to find meaningful clusters",
        "source": "raw"
    }
}