{
    "action": "tool_call",
    "reasoning": "My previous reply was prose and the numbers in it are not backed by evidence I can see. I'll recompute the summary for all nine numeric columns in one query, parsing the VARCHAR columns safely, so the results can be rendered and cited.",
    "tool": "query_sql",
    "args": {
        "sql": "WITH t 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), u AS (SELECT 'Age' AS metric, Age AS v FROM t UNION ALL SELECT 'Monthly_Income', Monthly_Income FROM t UNION ALL SELECT 'AI_Usage_Hours_Per_Day', AI_Usage_Hours_Per_Day FROM t UNION ALL SELECT 'Monthly_AI_Cost', Monthly_AI_Cost FROM t UNION ALL SELECT 'Productivity_Score', Productivity_Score FROM t UNION ALL SELECT 'Accuracy_Rating', Accuracy_Rating FROM t UNION ALL SELECT 'Satisfaction_Score', Satisfaction_Score FROM t UNION ALL SELECT 'Time_Saved_Hours_Per_Week', Time_Saved_Hours_Per_Week FROM t UNION ALL SELECT 'Work_or_Study_Hours_Per_Day', Work_or_Study_Hours_Per_Day FROM t) SELECT metric, COUNT(v) AS valid_n, 300 - COUNT(v) AS missing_n, ROUND(MIN(v),2) AS min_v, ROUND(quantile_cont(v,0.25),2) AS p25, ROUND(MEDIAN(v),2) AS median_v, ROUND(AVG(v),2) AS mean_v, ROUND(quantile_cont(v,0.75),2) AS p75, ROUND(quantile_cont(v,0.95),2) AS p95, ROUND(MAX(v),2) AS max_v, ROUND(STDDEV(v),2) AS sd_v FROM u GROUP BY metric ORDER BY metric",
        "purpose": "Summary statistics (count, missing, min, quartiles, mean, p95, max, SD) for all nine numeric measures, parsing text columns safely",
        "source": "raw"
    }
}