{
    "action": "tool_call",
    "reasoning": "I have correlations for numeric/categorical pairs, but I should also check: (1) era vs year (should be strong since era is derived from year ranges), (2) event_type vs gender (to see if certain event types are gender-specific), and (3) country vs is_china (deterministic relationship). Let me compute these final correlations to complete the picture.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT CORR(CASE WHEN era = 'Pre-reform (1988-2004)' THEN 1 WHEN era = 'Reform era (2008-2016)' THEN 2 ELSE 3 END, year) as era_year_corr, CORR(CASE WHEN event_type = 'Singles' THEN 1 WHEN event_type = 'Doubles' THEN 2 ELSE 3 END, CASE WHEN gender = 'Men' THEN 1 WHEN gender = 'Women' THEN 2 ELSE 3 END) as eventtype_gender_corr, CORR(CASE WHEN country = 'China' THEN 1 ELSE 0 END, is_china) as country_china_corr FROM raw.olympic_table_tennis_medals_1988_2024",
        "purpose": "Compute final set of correlations: era vs year, event_type vs gender, and country vs is_china to complete the correlation analysis",
        "source": "raw"
    }
}