{
    "action": "tool_call",
    "reasoning": "My earlier draft cited numbers that are not in the evidence, so I'm discarding it. I'll compute the numeric profile with one query that parses the VARCHAR columns with TRY_CAST and gives n, min, median, mean, p95, max and stddev per column.",
    "tool": "query_sql",
    "args": {
        "source": "raw",
        "purpose": "Distribution summary (count, min, median, mean, p95, max, stddev) for all numeric columns, parsing VARCHAR columns safely",
        "sql": "WITH p AS (SELECT TRY_CAST(REPLACE(REPLACE(Age,'$',''),',','') AS DOUBLE) AS Age, TRY_CAST(REPLACE(REPLACE(Monthly_Income,'$',''),',','') AS DOUBLE) AS Monthly_Income, TRY_CAST(REPLACE(REPLACE(AI_Usage_Hours_Per_Day,'$',''),',','') AS DOUBLE) AS AI_Usage_Hours_Per_Day, TRY_CAST(REPLACE(REPLACE(Monthly_AI_Cost,'$',''),',','') AS DOUBLE) AS Monthly_AI_Cost, CAST(Productivity_Score AS DOUBLE) AS Productivity_Score, CAST(Accuracy_Rating AS DOUBLE) AS Accuracy_Rating, CAST(Satisfaction_Score AS DOUBLE) AS Satisfaction_Score, TRY_CAST(REPLACE(REPLACE(Time_Saved_Hours_Per_Week,'$',''),',','') AS DOUBLE) AS Time_Saved_Hours_Per_Week, TRY_CAST(REPLACE(REPLACE(Work_or_Study_Hours_Per_Day,'$',''),',','') AS DOUBLE) AS Work_or_Study_Hours_Per_Day FROM raw.AI_Usage_and_Impact_on_Students_and_Professionals) SELECT 'Age' AS col, COUNT(Age) AS n, MIN(Age) AS min_v, MEDIAN(Age) AS median_v, AVG(Age) AS mean_v, quantile_cont(Age,0.95) AS p95, MAX(Age) AS max_v, STDDEV(Age) AS std_v FROM p UNION ALL SELECT 'Monthly_Income', COUNT(Monthly_Income), MIN(Monthly_Income), MEDIAN(Monthly_Income), AVG(Monthly_Income), quantile_cont(Monthly_Income,0.95), MAX(Monthly_Income), STDDEV(Monthly_Income) FROM p UNION ALL SELECT 'AI_Usage_Hours_Per_Day', COUNT(AI_Usage_Hours_Per_Day), MIN(AI_Usage_Hours_Per_Day), MEDIAN(AI_Usage_Hours_Per_Day), AVG(AI_Usage_Hours_Per_Day), quantile_cont(AI_Usage_Hours_Per_Day,0.95), MAX(AI_Usage_Hours_Per_Day), STDDEV(AI_Usage_Hours_Per_Day) FROM p UNION ALL SELECT 'Monthly_AI_Cost', COUNT(Monthly_AI_Cost), MIN(Monthly_AI_Cost), MEDIAN(Monthly_AI_Cost), AVG(Monthly_AI_Cost), quantile_cont(Monthly_AI_Cost,0.95), MAX(Monthly_AI_Cost), STDDEV(Monthly_AI_Cost) FROM p UNION ALL SELECT 'Productivity_Score', COUNT(Productivity_Score), MIN(Productivity_Score), MEDIAN(Productivity_Score), AVG(Productivity_Score), quantile_cont(Productivity_Score,0.95), MAX(Productivity_Score), STDDEV(Productivity_Score) FROM p UNION ALL SELECT 'Accuracy_Rating', COUNT(Accuracy_Rating), MIN(Accuracy_Rating), MEDIAN(Accuracy_Rating), AVG(Accuracy_Rating), quantile_cont(Accuracy_Rating,0.95), MAX(Accuracy_Rating), STDDEV(Accuracy_Rating) FROM p UNION ALL SELECT 'Satisfaction_Score', COUNT(Satisfaction_Score), MIN(Satisfaction_Score), MEDIAN(Satisfaction_Score), AVG(Satisfaction_Score), quantile_cont(Satisfaction_Score,0.95), MAX(Satisfaction_Score), STDDEV(Satisfaction_Score) FROM p UNION ALL SELECT 'Time_Saved_Hours_Per_Week', COUNT(Time_Saved_Hours_Per_Week), MIN(Time_Saved_Hours_Per_Week), MEDIAN(Time_Saved_Hours_Per_Week), AVG(Time_Saved_Hours_Per_Week), quantile_cont(Time_Saved_Hours_Per_Week,0.95), MAX(Time_Saved_Hours_Per_Week), STDDEV(Time_Saved_Hours_Per_Week) FROM p UNION ALL SELECT 'Work_or_Study_Hours_Per_Day', COUNT(Work_or_Study_Hours_Per_Day), MIN(Work_or_Study_Hours_Per_Day), MEDIAN(Work_or_Study_Hours_Per_Day), AVG(Work_or_Study_Hours_Per_Day), quantile_cont(Work_or_Study_Hours_Per_Day,0.95), MAX(Work_or_Study_Hours_Per_Day), STDDEV(Work_or_Study_Hours_Per_Day) FROM p"
    }
}