{
    "action": "tool_call",
    "reasoning": "To propose interesting analytical questions, I need to understand the dataset's actual characteristics beyond the card. Let me profile key dimensions to see what patterns exist: salary ranges, geographic distribution, temporal coverage, company sizes, remote work patterns, and education requirements. This will ground my question proposals in real data characteristics.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_jobs, COUNT(DISTINCT company_name) as unique_companies, COUNT(DISTINCT job_title) as unique_job_titles, COUNT(DISTINCT industry) as unique_industries, COUNT(DISTINCT company_location) as unique_locations, COUNT(DISTINCT employee_residence) as unique_residences, MIN(salary_usd) as min_salary, MAX(salary_usd) as max_salary, ROUND(AVG(salary_usd), 0) as avg_salary, ROUND(MEDIAN(salary_usd), 0) as median_salary, MIN(posting_date) as earliest_posting, MAX(posting_date) as latest_posting, COUNT(DISTINCT remote_ratio) as remote_options, COUNT(DISTINCT experience_level) as experience_levels, COUNT(DISTINCT education_required) as education_levels, COUNT(DISTINCT employment_type) as employment_types, MIN(years_experience) as min_experience, MAX(years_experience) as max_experience, ROUND(AVG(benefits_score), 2) as avg_benefits_score FROM raw.ai_job_dataset",
        "purpose": "Profile key dimensions to understand dataset characteristics for proposing analytical questions"
    }
}