{
    "action": "tool_call",
    "reasoning": "The question asks about natural segmentation or grouping of records. 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). I also have spatial dimensions (City ~147 distinct, Country Code ~17 distinct, Locality ~1,237 distinct). To identify natural segments, I should examine the distribution of records across these key dimensions. Let me start by looking at the geographic distribution by country and city, which are likely primary segmentation axes.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT \"Country Code\", COUNT(*) as restaurant_count, COUNT(DISTINCT \"City\") as cities, COUNT(DISTINCT \"Cuisines\") as cuisine_types, 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": "Examine geographic distribution by country to identify primary market segments",
        "source": "raw"
    }
}