{
    "action": "tool_call",
    "reasoning": "My earlier answer was malformed and I can't see its evidence in this turn, so I'm re-running one compact profiling query over the raw table to ground the data-quality findings before answering.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) AS n, COUNT(DISTINCT Student_ID) AS distinct_ids, COUNT(Student_ID) AS nonnull_ids, MIN(LENGTH(Student_ID)) AS min_id_len, MAX(LENGTH(Student_ID)) AS max_id_len, MIN(Student_ID) AS min_id, MAX(Student_ID) AS max_id, MIN(Age) AS min_age, MAX(Age) AS max_age, MIN(Daily_Usage_Hours) AS min_daily, MAX(Daily_Usage_Hours) AS max_daily, MIN(Weekend_Extra_Hours) AS min_wk, MAX(Weekend_Extra_Hours) AS max_wk, MIN(Sleep_Duration_Hours) AS min_sleep, MAX(Sleep_Duration_Hours) AS max_sleep, MIN(Sleep_Quality_Score) AS min_sq, MAX(Sleep_Quality_Score) AS max_sq, MIN(Mental_Health_Index) AS min_mh, MAX(Mental_Health_Index) AS max_mh, MIN(Perceived_Stress_Score) AS min_stress, MAX(Perceived_Stress_Score) AS max_stress, MIN(Academic_Performance_GPA) AS min_gpa, MAX(Academic_Performance_GPA) AS max_gpa, COUNT(*) - COUNT(Gender) AS null_gender, COUNT(*) - COUNT(Academic_Level) AS null_level, COUNT(*) - COUNT(Primary_Platform) AS null_platform, COUNT(*) - COUNT(Device_Type) AS null_device, COUNT(*) - COUNT(Late_Night_Usage) AS null_late, COUNT(*) - COUNT(Social_Comparison_Frequency) AS null_comp, COUNT(*) - COUNT(Overall_Impact) AS null_impact, COUNT(*) - COUNT(Daily_Usage_Hours) AS null_daily, COUNT(*) - COUNT(Sleep_Duration_Hours) AS null_sleep, COUNT(*) - COUNT(Perceived_Stress_Score) AS null_stress, COUNT(*) - COUNT(Mental_Health_Index) AS null_mh, COUNT(*) - COUNT(Academic_Performance_GPA) AS null_gpa, SUM(CASE WHEN Daily_Usage_Hours + Sleep_Duration_Hours > 20 THEN 1 ELSE 0 END) AS usage_plus_sleep_gt20 FROM raw.Social_media_impact_on_life",
        "purpose": "Single-pass data quality profile: key uniqueness, ID format, ranges, null counts, and a logical consistency check",
        "source": "raw"
    }
}