I'll systematically check for data quality issues across the different column types in this dataset. {"action": "tool_call", "reasoning": "Check for potential outliers and impossible values in the year column, and examine the distribution of medal_value to see if the 1-3 range is consistently used", "tool": "query_sql", "args": {"sql": "SELECT year, COUNT(*) as medal_count, MIN(medal_value) as min_val, MAX(medal_value) as max_val FROM raw.olympic_table_tennis_medals_1988_2024 GROUP BY year ORDER BY year", "purpose": "Check year distribution and medal_value consistency across years", "source": "raw"}}