The first MCP server for sports betting. Give any AI agent live access to picks, odds, game analysis, and performance tracking across NBA, NHL, NCAAB, and MLB.

mcp-name: io.github.seang1121/sports-betting-mcp

Connects any MCP-compatible AI agent to a live sports betting analysis system. Every pick is logged before tip-off and resolved against final scores. Nothing is cherry-picked.

All results are queryable in real-time via the get_win_rate tool. Numbers update after every game.

pip install sports-betting-mcp

Drop this into your MCP config (Claude Desktop, Cursor, Windsurf, Claude Code, etc.):

{
  "mcpServers": {
    "sports-betting": {
      "command": "sports-betting-mcp",
      "env": {
        "SPORTS_BETTING_API_URL": "https://sportsbettingaianalyzer.com",
        "SPORTS_BETTING_API_KEY": "your_api_key"
      }
    }
  }
}

Any client that supports the Model Context Protocol:

12 tools. Every call returns structured data that AI agents can reason over, display, or act on.

Each game runs through a multi-agent pipeline:

1. 12 specialized agents evaluate the game independently -- covering momentum, matchups, injuries, rest, travel, public betting percentages, sharp money indicators, historical trends, and more.
2. A consensus engine synthesizes all 12 opinions into a single pick with a confidence score.
3. Edge calculation compares the model's implied probability against the current market line.
4. Picks are logged before tip-off and resolved against final scores. No retroactive edits.

The confidence score and edge breakdown are included in every pick response, so your AI agent can filter, rank, or explain the reasoning behind any recommendation.

The API has two tiers: Free (any API key) and Pro (approved access).

Every pick from /xk/full includes:

{
  "ai_pick": "Lakers -5.5",
  "ai_verdict": "STRONG BET",
  "ai_probability": "65%",
  "ai_reasoning": "Lakers defense ranks top 5, opponent on B2B...",
  "edges": [13 individual edge factors],
  "top_edges": [top 5 positive],
  "negative_edges": [top 3 negative],
  "scorecard": [3 model scores],
  "fade_pick": "Celtics +5.5",
  "fade_team": "Celtics",
  "fade_odds": "+105",
  "fade_note": "Betting AGAINST the AI — take Celtics +5.5",
  "line_moved": true,
  "flags": ["line_moved"],
  "data_quality": "good"
}

When logging picks, include pick_source to track decision types:

• model_agree — following the AI's pick
• flip — fading/betting against the AI
• manual_override — custom pick

This enables win rate comparison between model-agree vs flip picks over time.

Authenticate with X-API-Key header. Get a free key at sportsbettingaianalyzer.com. Pro access requires admin approval.

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MIT