I need to identify the main categorical columns in this dataset and find their most common values with frequencies. Let me start by examining what categorical columns exist across the key tables. From the evidence so far, I can see: - **beer_ratings** has `language` as a categorical column (already have top 10 values) - **beers** has several categorical columns: `style_id`, `is_seasonal`, `Unrateable`, `is_one_off`, `is_alias`, `is_verified`, `is_retired`, `has_pic` Let me get the frequency distributions for the main categorical columns I haven't covered yet. {"action": "tool_call", "reasoning": "Need to get the most common beer styles (style_id) and their frequencies from the beers table, as this is a key categorical dimension for beer products", "tool": "query_sql", "args": {"sql": "SELECT style_id, COUNT(*) as frequency FROM raw.beers GROUP BY style_id ORDER BY frequency DESC LIMIT 20", "purpose": "Get top 20 most common beer styles by frequency"}}