{
    "action": "tool_call",
    "reasoning": "The question asks about natural segmentation opportunities in the dataset. I have the column metadata showing several strong classifiers (Cuisines ~1,851 distinct, Currency ~11 distinct, Rating color ~6 distinct, Rating text ~6 distinct, Price range ~4 distinct, and boolean flags for delivery/booking). I also see spatial dimensions (City ~147 distinct, Locality ~1,237 distinct, Country Code ~17 distinct) and measures (Aggregate rating, Average Cost for two, Votes). To identify natural segments, I should examine the distribution of records across these key dimensions. Let me start by profiling the main categorical dimensions to see which create meaningful, balanced groups. I'll query the distribution across the most promising segmentation candidates: Country Code, City (top cities), Price range, Rating text, and the service capability flags.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT Country_Code, COUNT(*) as restaurant_count, COUNT(DISTINCT City) as cities, ROUND(AVG(Aggregate_rating), 2) as avg_rating, ROUND(AVG(Average_Cost_for_two), 0) as avg_cost FROM raw.\"Zomato Restaurant Dataset\" GROUP BY Country_Code ORDER BY restaurant_count DESC",
        "purpose": "Profile distribution of restaurants by country to identify geographic segmentation",
        "source": "raw"
    }
}