{
    "action": "tool_call",
    "reasoning": "I need to identify the main categorical columns across the dataset. From step-2, I can see columns with rough_kind='categorical' or semantic_role='classifier'. However, I should query the metadata systematically to find all categorical columns with their distinct counts, then profile the top values for the most important ones. Let me start by querying analytics.column_overview to find categorical columns ordered by their importance (based on row count of their tables and distinctness).",
    "tool": "query_sql",
    "sql": "SELECT co.table_name, co.column_name, co.rough_kind, co.semantic_role, co.distinct_count, co.approx_distinct_count, to.row_count FROM analytics.column_overview co JOIN analytics.table_overview to ON co.table_name = to.table_name WHERE co.rough_kind = 'categorical' OR co.semantic_role = 'classifier' ORDER BY to.row_count DESC, co.approx_distinct_count DESC LIMIT 50",
    "purpose": "Identify the main categorical columns across the dataset by joining column metadata with table sizes",
    "args": {
        "sql": "SELECT co.table_name, co.column_name, co.rough_kind, co.semantic_role, co.distinct_count, co.approx_distinct_count, to.row_count FROM analytics.column_overview co JOIN analytics.table_overview to ON co.table_name = to.table_name WHERE co.rough_kind = 'categorical' OR co.semantic_role = 'classifier' ORDER BY to.row_count DESC, co.approx_distinct_count DESC LIMIT 50",
        "purpose": "Identify the main categorical columns across the dataset by joining column metadata with table sizes"
    }
}