```json { "action": "tool_call", "reasoning": "The evidence shows 2010_2026_scores has 13 columns. I can see approximate distinct counts for the categorical columns from the inspect_columns result: Week (~28), GameStatus (~4), GameSlot (~13), AwayTeam (~39), HomeTeam (~39). However, to provide exact distinct counts for all categorical columns in one comprehensive query, I should run a single SQL query that computes COUNT(DISTINCT col) for each categorical column. This will give precise numbers rather than the approximate counts from the metadata.", "tool": "query_sql", "args": { "sql": "SELECT COUNT(DISTINCT Season) as Season_distinct, COUNT(DISTINCT Week) as Week_distinct, COUNT(DISTINCT GameStatus) as GameStatus_distinct, COUNT(DISTINCT GameSlot) as GameSlot_distinct, COUNT(DISTINCT GameDate) as GameDate_distinct, COUNT(DISTINCT AwayTeam) as AwayTeam_distinct, COUNT(DISTINCT HomeTeam) as HomeTeam_distinct FROM raw.2010_2026_scores", "purpose": "Get exact distinct counts for all categorical/dimensional columns in the scores table", "source": "raw" } } ```