{
    "action": "tool_call",
    "reasoning": "My earlier reply cited sample queries and numbers that aren't in the evidence, so I'm discarding it. I'll start with one profiling query over the raw table to ground the data-quality and outcome-score claims: row count, distinct User_ID, nulls and score averages.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) AS total_rows, COUNT(DISTINCT User_ID) AS distinct_users, COUNT(Productivity_Score) AS productivity_nonnull, COUNT(Accuracy_Rating) AS accuracy_nonnull, COUNT(Satisfaction_Score) AS satisfaction_nonnull, ROUND(AVG(Productivity_Score), 2) AS avg_productivity, MIN(Productivity_Score) AS min_productivity, MAX(Productivity_Score) AS max_productivity, ROUND(AVG(Satisfaction_Score), 2) AS avg_satisfaction, ROUND(AVG(Accuracy_Rating), 2) AS avg_accuracy FROM raw.AI_Usage_and_Impact_on_Students_and_Professionals",
        "purpose": "Profile row count, duplicate User_IDs, null counts and average outcome scores",
        "source": "raw"
    }
}