{
    "action": "tool_call",
    "reasoning": "Since there's no research report, I need to understand the dataset's actual characteristics to propose interesting questions. Let me start by profiling the key dimensions and measures to see what patterns exist. I'll query for: 1) temporal coverage and distribution, 2) salary ranges and distribution, 3) diversity of job titles, industries, and companies, 4) remote work patterns, and 5) geographic spread. This will reveal what makes this dataset analytically rich.",
    "tool": "query_sql",
    "purpose": "Profile key dimensions to understand what analytical questions the dataset can support",
    "sql": "SELECT COUNT(*) as total_jobs, MIN(posting_date) as earliest_posting, MAX(posting_date) as latest_posting, MIN(application_deadline) as earliest_deadline, MAX(application_deadline) as latest_deadline, COUNT(DISTINCT job_title) as distinct_job_titles, COUNT(DISTINCT company_name) as distinct_companies, COUNT(DISTINCT industry) as distinct_industries, COUNT(DISTINCT company_location) as distinct_locations, COUNT(DISTINCT employee_residence) as distinct_residences, MIN(salary_usd) as min_salary, MAX(salary_usd) as max_salary, MEDIAN(salary_usd) as median_salary, AVG(salary_usd) as avg_salary, MIN(years_experience) as min_experience, MAX(years_experience) as max_experience, MEDIAN(years_experience) as median_experience, COUNT(DISTINCT remote_ratio) as remote_options, COUNT(DISTINCT experience_level) as experience_levels, COUNT(DISTINCT education_required) as education_levels FROM raw.ai_job_dataset"
}