{
    "action": "tool_call",
    "reasoning": "My earlier answer was cut off and I can't see its supporting evidence in this context. I'll re-verify the key data-quality figures in a single profiling query before finalizing: key uniqueness, nulls, the age and academic-level conflict, and the range checks.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) AS n_rows, COUNT(DISTINCT Student_ID) AS distinct_ids, COUNT(*) - COUNT(Academic_Performance_GPA) AS gpa_nulls, COUNT(*) - COUNT(Perceived_Stress_Score) AS stress_nulls, SUM(CASE WHEN Academic_Level = 'High School' AND Age >= 19 THEN 1 ELSE 0 END) AS hs_age_19_plus, SUM(CASE WHEN Academic_Level = 'High School' THEN 1 ELSE 0 END) AS hs_rows, SUM(CASE WHEN Daily_Usage_Hours + Sleep_Duration_Hours > 20 THEN 1 ELSE 0 END) AS time_budget_over_20, MIN(Age) AS min_age, MAX(Age) AS max_age, MIN(Sleep_Quality_Score) AS min_sleepq, MAX(Sleep_Quality_Score) AS max_sleepq, MIN(Perceived_Stress_Score) AS min_stress, MAX(Perceived_Stress_Score) AS max_stress, MIN(Mental_Health_Index) AS min_mh, MAX(Mental_Health_Index) AS max_mh, MIN(Academic_Performance_GPA) AS min_gpa, MAX(Academic_Performance_GPA) AS max_gpa, MIN(Daily_Usage_Hours) AS min_use, MAX(Daily_Usage_Hours) AS max_use, MIN(Sleep_Duration_Hours) AS min_sleep, MAX(Sleep_Duration_Hours) AS max_sleep FROM raw.Social_media_impact_on_life",
        "purpose": "Re-verify data-quality figures: uniqueness, nulls, age/level conflicts, and value ranges",
        "source": "raw"
    }
}