```json { "action": "tool_call", "reasoning": "I need to quantify exactly how many values in the supposedly numeric/temporal columns (Age, Score, Date) are actually parseable as their intended types versus completely malformed. This will ground the claim that 100% are invalid with specific counts.", "tool": "query_sql", "args": { "sql": "SELECT COUNT(*) as total_rows, COUNT(CASE WHEN TRY_CAST(Age AS INTEGER) IS NOT NULL THEN 1 END) as age_parseable_as_int, COUNT(CASE WHEN TRY_CAST(Score AS DECIMAL) IS NOT NULL THEN 1 END) as score_parseable_as_decimal, COUNT(CASE WHEN TRY_CAST(Date AS DATE) IS NOT NULL THEN 1 END) as date_parseable_as_date, COUNT(CASE WHEN Age ~ '^-?[0-9]+$' THEN 1 END) as age_looks_like_integer, COUNT(CASE WHEN Score ~ '^-?[0-9]+(\\.[0-9]+)?$' THEN 1 END) as score_looks_like_number FROM raw.\"Dummy Data\"", "purpose": "Quantify how many values in Age, Score, and Date columns can be parsed as their intended types (integer, decimal, date) to verify the extent of data corruption", "source": "raw" } } ```