{
    "action": "tool_call",
    "reasoning": "My previous reply cited figures I had not computed, so I'm discarding it. I'll start by profiling the raw table: row count, distinct User_IDs, and null counts for the key columns, all in one query.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) AS n_rows, COUNT(DISTINCT User_ID) AS distinct_users, COUNT(Productivity_Score) AS prod_nonnull, COUNT(Accuracy_Rating) AS acc_nonnull, COUNT(Satisfaction_Score) AS sat_nonnull, COUNT(AI_Tool) AS tool_nonnull, COUNT(AI_Purpose) AS purpose_nonnull, COUNT(Country) AS country_nonnull, COUNT(User_Type) AS usertype_nonnull, MIN(Productivity_Score) AS prod_min, MAX(Productivity_Score) AS prod_max, AVG(Productivity_Score) AS prod_avg, AVG(Satisfaction_Score) AS sat_avg, AVG(Accuracy_Rating) AS acc_avg FROM raw.AI_Usage_and_Impact_on_Students_and_Professionals",
        "purpose": "Baseline profile: size, key uniqueness, null counts and score ranges",
        "source": "raw"
    }
}