{
    "action": "tool_call",
    "reasoning": "I have overview data showing sector, GPU supplier, and chip type distributions. To identify natural segmentation opportunities, I should examine the temporal dimension (operational dates) to see if there are distinct eras or waves of deployment, and look at the geographic distribution. I'll also check the 'Certainty' and 'Include in Standard Analysis' flags to understand data quality segmentation. Let me start by examining the temporal distribution and key categorical dimensions together.",
    "tool": "query_sql",
    "purpose": "Examine temporal distribution of systems by year and key dimensions to identify natural segmentation patterns",
    "args": {
        "sql": "SELECT EXTRACT(YEAR FROM \"First Operational Date\") as year, COUNT(*) as systems, COUNT(DISTINCT \"Chip type (primary)\") as chip_types, COUNT(DISTINCT \"GPU Supplier (primary)\") as gpu_suppliers, AVG(\"Max OP/s (log)\") as avg_compute_log, SUM(\"Total number of AI chips\") as total_chips FROM raw.ai_supercomputers WHERE \"First Operational Date\" IS NOT NULL GROUP BY year ORDER BY year",
        "source": "raw"
    }
}