{
    "action": "tool_call",
    "reasoning": "I've already gathered substantial evidence about data quality: salary ranges, currency distributions, date validity, duplicates, and experience level consistency. Now I need to check for outliers in other numeric measures (benefits_score, job_description_length), verify the consistency of categorical fields (employment_type, company_size, remote_ratio values), and check for any format inconsistencies in text fields like company_location and employee_residence.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_rows, MIN(benefits_score) as min_benefits, MAX(benefits_score) as max_benefits, AVG(benefits_score) as avg_benefits, COUNT(DISTINCT benefits_score) as distinct_benefits, MIN(job_description_length) as min_desc_len, MAX(job_description_length) as max_desc_len, AVG(job_description_length) as avg_desc_len, COUNT(DISTINCT job_description_length) as distinct_desc_len, COUNT(DISTINCT employment_type) as emp_types, COUNT(DISTINCT company_size) as company_sizes, COUNT(DISTINCT remote_ratio) as remote_ratios FROM raw.ai_job_dataset",
        "purpose": "Check ranges and distinct values for benefits_score, job_description_length, and verify categorical field cardinality",
        "source": "raw"
    }
}