{
    "action": "tool_call",
    "reasoning": "I've already gathered extensive metadata about the dataset. Now I need to check for specific data quality issues. Let me examine: 1) Outliers in salary_usd (I see min=32519, max=399095, but need to check distribution extremes), 2) Consistency of date ranges (posting_date vs application_deadline logic), 3) Experience level vs years_experience alignment (I see EN has 0-1 years which is correct, but need to verify no mismatches), 4) Remote_ratio values (should be 0, 50, or 100 based on distinct count of 3), 5) Benefits_score range (5.0-10.0 seems reasonable but let me verify no impossible values). I'll write one comprehensive query to check for logical inconsistencies and outliers.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as deadline_before_posting, MIN(application_deadline - posting_date) as min_days_diff, MAX(application_deadline - posting_date) as max_days_diff FROM raw.ai_job_dataset WHERE application_deadline < posting_date UNION ALL SELECT COUNT(*) as exp_mismatch_count, MIN(years_experience) as min_years_in_mismatch, MAX(years_experience) as max_years_in_mismatch FROM raw.ai_job_dataset WHERE (experience_level = 'EN' AND years_experience > 1) OR (experience_level = 'MI' AND (years_experience < 2 OR years_experience > 4)) OR (experience_level = 'SE' AND (years_experience < 5 OR years_experience > 9)) OR (experience_level = 'EX' AND years_experience < 10)",
        "purpose": "Check for logical inconsistencies: deadlines before posting dates, and experience level mismatches with years_experience",
        "source": "raw"
    }
}