{
    "action": "tool_call",
    "reasoning": "The question asks about natural segmentation/grouping of records. The overview confirms this is a single-table dataset with passenger records. I need to understand what categorical and hierarchical dimensions exist that would create meaningful segments. The card shows several classifiers (Cabin, Parch, Pclass, Sex, Survived) and a spatial axis (Embarked). I should examine the distribution and cardinality of these grouping variables to identify the most natural segmentation approaches. Let me start by profiling the key categorical columns to see their distributions and how they might segment the 891 passengers.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_passengers, COUNT(DISTINCT Pclass) as classes, COUNT(DISTINCT Sex) as genders, COUNT(DISTINCT Embarked) as embarkation_ports, COUNT(DISTINCT Survived) as survival_outcomes, COUNT(DISTINCT Cabin) as unique_cabins, COUNT(DISTINCT Parch) as parent_child_values, COUNT(DISTINCT SibSp) as sibling_spouse_values FROM raw.titanic",
        "purpose": "Get cardinality of key categorical dimensions to understand natural grouping variables",
        "source": "raw"
    }
}