{
    "action": "tool_call",
    "reasoning": "I have good evidence about temporal and categorical distributions from the overview and prior queries. Now I need to examine the actual ranking structure and points distribution to identify natural segmentation boundaries. Let me look at the distribution of ranking points and how they cluster, as this is likely the most meaningful way to segment competitive tiers beyond just rank numbers.",
    "tool": "query_sql",
    "purpose": "Analyze the distribution of ranking points to identify natural competitive tier boundaries",
    "args": {
        "sql": "SELECT CASE WHEN Points >= 3000 THEN 'Elite (3000+)' WHEN Points >= 2000 THEN 'Top Tier (2000-2999)' WHEN Points >= 1000 THEN 'Mid Tier (1000-1999)' WHEN Points >= 500 THEN 'Competitive (500-999)' WHEN Points >= 100 THEN 'Developing (100-499)' ELSE 'Emerging (0-99)' END AS points_tier, COUNT(*) AS record_count, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) AS pct, MIN(Points) AS min_points, MAX(Points) AS max_points, ROUND(AVG(Points), 0) AS avg_points FROM raw.ittf_rankings GROUP BY points_tier ORDER BY min_points DESC",
        "source": "raw"
    }
}