{
    "action": "tool_call",
    "reasoning": "My previous draft cited numbers (distinct counts, ranges, nulls) that I can't see in the evidence, so I need to compute them first. One profiling query over all relevant columns will ground the column classification.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) AS n_rows, COUNT(DISTINCT Student_ID) AS distinct_ids, COUNT(Student_ID) AS nn_id, COUNT(DISTINCT Age) AS d_age, MIN(Age) AS min_age, MAX(Age) AS max_age, COUNT(DISTINCT Gender) AS d_gender, COUNT(DISTINCT Academic_Level) AS d_level, COUNT(DISTINCT Primary_Platform) AS d_platform, COUNT(DISTINCT Device_Type) AS d_device, COUNT(DISTINCT Social_Comparison_Frequency) AS d_comp, COUNT(DISTINCT Overall_Impact) AS d_impact, COUNT(DISTINCT Late_Night_Usage) AS d_late, COUNT(DISTINCT Sleep_Quality_Score) AS d_sleepq, 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, COUNT(*)-COUNT(Perceived_Stress_Score) AS null_stress, MIN(Perceived_Stress_Score) AS min_stress, MAX(Perceived_Stress_Score) AS max_stress, COUNT(*)-COUNT(Mental_Health_Index) AS null_mh, MIN(Mental_Health_Index) AS min_mh, MAX(Mental_Health_Index) AS max_mh, COUNT(*)-COUNT(Academic_Performance_GPA) AS null_gpa, MIN(Academic_Performance_GPA) AS min_gpa, MAX(Academic_Performance_GPA) AS max_gpa FROM raw.Social_media_impact_on_life",
        "purpose": "Profile all columns in one pass to classify them as identifier, categorical dimension, or numeric metric",
        "source": "raw"
    }
}