{
    "action": "tool_call",
    "reasoning": "The dataset appears to be a CSV with embedded report sections (headers like '=== DAILY TREND — LAST 30 DAYS ===') mixed into the data rows. I need to understand the actual structure by examining the raw data more carefully. The inspect_columns result shows all columns are VARCHAR, which suggests the numeric values are stored as text. I should query the raw data to identify: 1) rows that are actual data vs. report headers/totals, 2) data type inconsistencies (percentages with '%', numbers as text), 3) impossible values (negative counts, percentages >100%), 4) outliers in call volumes or resolution rates, and 5) the unnamed columns that appear empty.",
    "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 IN ('Thu', 'Fri', 'Sat', 'Sun', 'Mon', 'Tue', 'Wed', 'Today') THEN 'day_of_week' WHEN Day LIKE '%-%' THEN 'date' WHEN Day = 'TOTAL' THEN 'total_row' WHEN Day = 'Date' THEN 'header_row' WHEN Day = '' THEN 'blank_row' WHEN Day LIKE '===%' THEN 'section_header' ELSE 'other' END AS row_type FROM raw.callyeah_analytics_2026_07_23 LIMIT 50",
        "purpose": "Classify rows by type to identify data vs. metadata/headers, and examine the structure of the dataset including the unnamed columns"
    }
}