{
    "action": "tool_call",
    "reasoning": "The earlier draft cited figures (300 rows vs 288 distinct User_IDs, Gender label variants) that I can't see in the evidence, so I'm re-verifying the key data-quality claims before answering.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) AS total_rows, COUNT(DISTINCT User_ID) AS distinct_users, COUNT(DISTINCT Gender) AS distinct_gender_labels, COUNT(DISTINCT AI_Tool) AS distinct_tool_labels, COUNT(DISTINCT Would_Recommend) AS distinct_recommend_labels, SUM(CASE WHEN Gender IS NULL OR TRIM(Gender) = '' THEN 1 ELSE 0 END) AS blank_gender, ROUND(AVG(Productivity_Score), 2) AS avg_productivity, ROUND(AVG(Accuracy_Rating), 2) AS avg_accuracy, ROUND(AVG(Satisfaction_Score), 2) AS avg_satisfaction FROM raw.AI_Usage_and_Impact_on_Students_and_Professionals",
        "purpose": "Verify row count, duplicate User_IDs, label inconsistency and score averages",
        "source": "raw"
    }
}