{
    "action": "tool_call",
    "reasoning": "The evidence shows this dataset contains multiple record types mixed together: individual call records (150), date values (30), daily summaries (7), aggregate totals (3), headers (5), and blank rows (8). To identify natural segmentation opportunities, I need to examine the actual structure of these different record types more closely. Let me query to see examples of each type and understand what distinguishes them, particularly looking at the patterns in the columns that would allow clean separation.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT Day, \"Total Calls\", \"AI Handled\", \"Human Handled\", Voicemail, \"AI Resolution %\", \"Human %\", _unnamed_7, _unnamed_8, _unnamed_9, CASE WHEN Day LIKE 'CY-%' THEN 'Call Record' WHEN Day IN ('Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun', 'Today') THEN 'Daily Summary' WHEN Day = 'TOTAL' THEN 'Aggregate Total' WHEN Day LIKE '%-%' AND Day NOT LIKE 'CY-%' THEN 'Date Value' WHEN Day = 'Call ID' THEN 'Header' WHEN Day = '' OR Day IS NULL THEN 'Blank' WHEN Day LIKE '%===%' THEN 'Section Header' ELSE 'Other' END AS record_type FROM raw.callyeah_analytics_2026_07_23 ORDER BY CASE WHEN Day LIKE 'CY-%' THEN 1 WHEN Day IN ('Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun', 'Today') THEN 2 WHEN Day = 'TOTAL' THEN 3 WHEN Day LIKE '%-%' AND Day NOT LIKE 'CY-%' THEN 4 WHEN Day = 'Call ID' THEN 5 ELSE 6 END, Day LIMIT 50",
        "purpose": "Examine the structure of different record types to understand segmentation patterns"
    }
}