{
    "action": "tool_call",
    "reasoning": "The question asks for natural segmentation approaches. I have basic country-level stats showing 15 countries with India (code 1) dominating at 8,652 restaurants. To identify meaningful segments, I should examine the key dimensions: cuisines (1,851 distinct), cities (147), price ranges (4), rating categories, and service features (online delivery, table booking). Let me start by looking at the distribution across rating categories and price ranges, which are likely to reveal natural customer/market segments.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT \"Rating text\", \"Price range\", COUNT(*) as restaurant_count, ROUND(AVG(\"Aggregate rating\"), 2) as avg_rating, ROUND(AVG(\"Average Cost for two\"), 0) as avg_cost, ROUND(AVG(\"Votes\"), 0) as avg_votes FROM raw.\"Zomato Restaurant Dataset\" GROUP BY \"Rating text\", \"Price range\" ORDER BY \"Rating text\", \"Price range\"",
        "purpose": "Examine the natural segmentation by rating quality and price range to identify market positioning clusters",
        "source": "raw"
    }
}