{
    "action": "tool_call",
    "reasoning": "I have basic dataset statistics showing 15K jobs across 16 companies, 20 job titles, 15 industries, with salary range $32K-$399K and dates from Jan 2024 to Apr 2025. To propose truly interesting analytical questions, I need to understand the distributions and relationships better. Let me examine: 1) How salary varies by key dimensions (experience, company size, remote work), 2) Temporal patterns in job postings and requirements, 3) Geographic patterns. I'll query for distributions across the main classifiers to see what analytical angles have depth.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT experience_level, COUNT(*) as job_count, ROUND(AVG(salary_usd), 0) as avg_salary, ROUND(MEDIAN(salary_usd), 0) as median_salary, ROUND(AVG(years_experience), 1) as avg_years_exp, ROUND(AVG(benefits_score), 2) as avg_benefits FROM raw.ai_job_dataset GROUP BY experience_level ORDER BY job_count DESC",
        "purpose": "Understand salary and benefits distribution by experience level to identify interesting compensation patterns",
        "source": "raw"
    }
}