I'll systematically check for data quality issues across the Titanic dataset by examining outliers, impossible values, and inconsistencies. ```json { "action": "tool_call", "reasoning": "First, I'll get a comprehensive statistical profile of all numeric columns in one query to identify potential outliers, impossible values, and range issues. This covers Age, Fare, SibSp, Parch, Pclass, Survived, and PassengerId.", "tool": "query_sql", "args": { "sql": "SELECT COUNT(*) as total_rows, COUNT(Age) as age_non_null, MIN(Age) as age_min, MAX(Age) as age_max, AVG(Age) as age_avg, MEDIAN(Age) as age_median, STDDEV(Age) as age_stddev, COUNT(Fare) as fare_non_null, MIN(Fare) as fare_min, MAX(Fare) as fare_max, AVG(Fare) as fare_avg, MEDIAN(Fare) as fare_median, STDDEV(Fare) as fare_stddev, COUNT(SibSp) as sibsp_non_null, MIN(SibSp) as sibsp_min, MAX(SibSp) as sibsp_max, AVG(SibSp) as sibsp_avg, COUNT(Parch) as parch_non_null, MIN(Parch) as parch_min, MAX(Parch) as parch_max, AVG(Parch) as parch_avg, COUNT(Pclass) as pclass_non_null, MIN(Pclass) as pclass_min, MAX(Pclass) as pclass_max, COUNT(DISTINCT Pclass) as pclass_distinct, COUNT(Survived) as survived_non_null, MIN(Survived) as survived_min, MAX(Survived) as survived_max, COUNT(DISTINCT Survived) as survived_distinct FROM raw.titanic", "purpose": "Get comprehensive statistics for all numeric columns to identify outliers, impossible values, and range issues", "source": "raw" } } ```