```json { "action": "tool_call", "reasoning": "The question asks for a classification of all columns across the dataset. The dataset has 451 tables with 1,822,394 rows total. To efficiently classify columns by their role (identifiers, categorical dimensions, numeric metrics, dates/times), I should use inspect_columns on the main tables. Looking at the dataset card, the largest tables are the yearly plays tables (2015_plays with 57,336 rows, etc.) and the 2010_2026_scores table (6,185 rows). I'll start by inspecting the 2010_2026_scores table since it appears to be a comprehensive scores table spanning multiple years, which will give me a good overview of the key column types in this NFL dataset.", "tool": "inspect_columns", "args": { "table": "2010_2026_scores" } } ```