{
    "action": "tool_call",
    "reasoning": "I need to systematically examine data quality issues. The evidence shows the Days column has mixed content (numeric values like '2,566', text like 'def.', '–', 'draw vs.', and even full date strings). I should profile all columns to identify: 1) type mismatches (numeric columns stored as text), 2) null/missing patterns, 3) value ranges and outliers, 4) format inconsistencies. Let me start with a comprehensive profile of the key data columns.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_rows, COUNT(DISTINCT No) as distinct_champions, COUNT(CASE WHEN Days = 'def.' THEN 1 END) as days_def, COUNT(CASE WHEN Days = '–' THEN 1 END) as days_dash, COUNT(CASE WHEN Days = '−' THEN 1 END) as days_minus, COUNT(CASE WHEN Days = 'draw vs.' THEN 1 END) as days_draw, COUNT(CASE WHEN Days = ' ' THEN 1 END) as days_blank, COUNT(CASE WHEN Days NOT IN ('def.', '–', '−', 'draw vs.', ' ') AND TRY_CAST(REPLACE(Days, ',', '') AS INTEGER) IS NULL THEN 1 END) as days_other_text, COUNT(CASE WHEN TRY_CAST(REPLACE(Days, ',', '') AS INTEGER) IS NOT NULL THEN 1 END) as days_numeric, COUNT(CASE WHEN End_reign = 'present' THEN 1 END) as end_present, COUNT(CASE WHEN Title_defenses = '–' THEN 1 END) as defenses_dash, COUNT(CASE WHEN Title_defenses = '−' THEN 1 END) as defenses_minus, COUNT(CASE WHEN Title_defenses = ' ' THEN 1 END) as defenses_blank FROM raw.championsdataset",
        "purpose": "Profile data quality issues: count mixed formats in Days column (numeric vs text placeholders), check End_reign for 'present' values, and examine Title_defenses for missing value indicators",
        "source": "raw"
    }
}