{
    "action": "tool_call",
    "reasoning": "I need to determine the most important numeric metric. From the evidence, I can see the dataset has three numeric measures: Age, Fare, and SibSp. Given this is Titanic data about survival outcomes, the most analytically important metric is likely Fare (ticket price), as it's a key indicator of passenger class/wealth and correlates with survival rates. I've already seen the highest fares (step-1: £512.33 for Ward, Cardeza, Lesurer) and lowest fares (step-2: £0 for 18 passengers). However, I should verify the full distribution and confirm Fare is indeed the most important metric by checking its range and variation compared to other measures.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT 'Age' as metric, MIN(Age) as min_value, MAX(Age) as max_value, AVG(Age) as avg_value, MEDIAN(Age) as median_value, STDDEV(Age) as stddev_value, COUNT(Age) as non_null_count, COUNT(*) - COUNT(Age) as null_count FROM raw.titanic UNION ALL SELECT 'Fare', MIN(Fare), MAX(Fare), AVG(Fare), MEDIAN(Fare), STDDEV(Fare), COUNT(Fare), COUNT(*) - COUNT(Fare) FROM raw.titanic UNION ALL SELECT 'SibSp', MIN(SibSp), MAX(SibSp), AVG(SibSp), MEDIAN(SibSp), STDDEV(SibSp), COUNT(SibSp), COUNT(*) - COUNT(SibSp) FROM raw.titanic ORDER BY stddev_value DESC",
        "purpose": "Compare the three numeric measures (Age, Fare, SibSp) to identify which has the greatest variation and analytical importance",
        "source": "raw"
    }
}