{
    "action": "tool_call",
    "reasoning": "To identify the most interesting analytical questions, I need to understand the dataset's geographic and market diversity. Let me check the distribution of restaurants across countries and cities, and examine the key service features to ground the questions in actual data patterns.",
    "tool": "query_sql",
    "purpose": "Get country distribution, top cities, and service feature adoption rates to identify interesting analytical angles",
    "args": {
        "sql": "SELECT COUNT(DISTINCT \"Country Code\") as countries, COUNT(DISTINCT \"City\") as cities, COUNT(*) as total_restaurants, COUNT(CASE WHEN \"Has Online delivery\" = 'Yes' THEN 1 END) as with_online_delivery, COUNT(CASE WHEN \"Has Table booking\" = 'Yes' THEN 1 END) as with_table_booking, COUNT(CASE WHEN \"Has Online delivery\" = 'Yes' AND \"Has Table booking\" = 'Yes' THEN 1 END) as with_both, ROUND(AVG(\"Aggregate rating\"), 2) as avg_rating, ROUND(AVG(\"Average Cost for two\"), 0) as avg_cost FROM raw.\"Zomato Restaurant Dataset\"",
        "source": "raw"
    }
}