{
    "action": "tool_call",
    "reasoning": "The evidence shows the dataset has several categorical dimensions that could serve as natural segmentation bases. I've seen class distribution (3 classes with 55% in 3rd class), and the card shows Sex, Survived, Embarked, Cabin, Parch as classifiers. To identify the most natural segmentation approach, I should examine survival rates across the key demographic and ticketing dimensions - particularly class, sex, and embarkation port - to see which create meaningful, actionable segments. Let me start by looking at survival rates by passenger class and sex together.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT Pclass, Sex, COUNT(*) as passengers, SUM(Survived) as survived, ROUND(100.0 * SUM(Survived) / COUNT(*), 1) as survival_rate FROM raw.titanic GROUP BY Pclass, Sex ORDER BY Pclass, Sex",
        "purpose": "Examine survival rates across class and sex to identify natural passenger segments",
        "source": "raw"
    }
}