I'll systematically check for data quality issues across the Iris dataset by examining the actual data for outliers, impossible values, and inconsistencies. ```json { "action": "tool_call", "reasoning": "First, I'll get a comprehensive profile of all numeric columns in one query to check for outliers, impossible values (like negative measurements), and statistical anomalies. This will show min/max/avg/median/stddev for all measurements, plus null counts.", "tool": "query_sql", "args": { "sql": "SELECT COUNT(*) as total_rows, COUNT(Id) as id_count, COUNT(SepalLengthCm) as sepal_length_count, COUNT(SepalWidthCm) as sepal_width_count, COUNT(PetalLengthCm) as petal_length_count, COUNT(PetalWidthCm) as petal_width_count, COUNT(Species) as species_count, MIN(SepalLengthCm) as sepal_length_min, MAX(SepalLengthCm) as sepal_length_max, AVG(SepalLengthCm) as sepal_length_avg, MEDIAN(SepalLengthCm) as sepal_length_median, STDDEV(SepalLengthCm) as sepal_length_stddev, MIN(SepalWidthCm) as sepal_width_min, MAX(SepalWidthCm) as sepal_width_max, AVG(SepalWidthCm) as sepal_width_avg, MEDIAN(SepalWidthCm) as sepal_width_median, STDDEV(SepalWidthCm) as sepal_width_stddev, MIN(PetalLengthCm) as petal_length_min, MAX(PetalLengthCm) as petal_length_max, AVG(PetalLengthCm) as petal_length_avg, MEDIAN(PetalLengthCm) as petal_length_median, STDDEV(PetalLengthCm) as petal_length_stddev, MIN(PetalWidthCm) as petal_width_min, MAX(PetalWidthCm) as petal_width_max, AVG(PetalWidthCm) as petal_width_avg, MEDIAN(PetalWidthCm) as petal_width_median, STDDEV(PetalWidthCm) as petal_width_stddev, COUNT(DISTINCT Species) as distinct_species FROM raw.Iris", "purpose": "Profile all numeric columns to detect nulls, impossible values, and statistical outliers", "source": "raw" } } ```