{
    "action": "tool_call",
    "reasoning": "I have basic profiling data showing no nulls and consistent medal_value ranges (1-3). Now I need to check for data quality issues more systematically. Let me examine: 1) temporal consistency (are years valid Olympic years?), 2) medal distribution anomalies, 3) event naming consistency, 4) country name formats, 5) medalist name formats, and 6) the is_china flag consistency with country='China'. I'll start by checking if years align with actual Olympic cycles and examining event/country/medalist values for formatting issues.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT year, COUNT(*) as medal_count, year % 4 as year_mod_4, CASE WHEN year IN (1988,1992,1996,2000,2004,2008,2012,2016,2020,2024) THEN 'valid' ELSE 'invalid' END as olympic_year_check FROM raw.olympic_table_tennis_medals_1988_2024 GROUP BY year ORDER BY year",
        "purpose": "Check if all years are valid Olympic years (should be every 4 years, with 2020 being exceptional due to COVID)",
        "source": "raw"
    }
}