{
    "action": "tool_call",
    "reasoning": "I've already examined score ranges and found no negative scores, with reasonable min/max values (0-59 away, 0-70 home). I saw 4 'extreme_scores' but need to understand what makes them extreme. Now I should check for data quality issues in the temporal and categorical columns: inconsistent date formats, invalid week values, team name variations, and the 837 null scores I saw in step-1. Let me profile the temporal and categorical columns for format consistency and validity.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_rows, COUNT(DISTINCT Season) as distinct_seasons, MIN(Season) as min_season, MAX(Season) as max_season, COUNT(DISTINCT Week) as distinct_weeks, COUNT(DISTINCT GameStatus) as distinct_statuses, COUNT(DISTINCT GameSlot) as distinct_slots, COUNT(DISTINCT AwayTeam) as distinct_away_teams, COUNT(DISTINCT HomeTeam) as distinct_home_teams, COUNT(CASE WHEN GameDate = '' THEN 1 END) as empty_dates, COUNT(CASE WHEN GameDate = 'TBD' THEN 1 END) as tbd_dates, COUNT(CASE WHEN AwayScore IS NULL THEN 1 END) as null_away_scores, COUNT(CASE WHEN HomeScore IS NULL THEN 1 END) as null_home_scores FROM raw.\"2010_2026_scores\"",
        "purpose": "Profile categorical and temporal columns for consistency and completeness",
        "source": "raw"
    }
}