# Clone repository
git clone https://github.com/roizenlabs/sportintel-mcp.git
cd sportintel-mcp

# Install dependencies
npm install

# Set up environment
cp .env.example .env
# Edit .env with your API keys

{
  "mcpServers": {
    "sportintel": {
      "command": "node",
      "args": ["/path/to/sportintel-mcp/dist/main.js"],
      "env": {
        "ODDS_API_KEY": "your_api_key_here"
      }
    }
  }
}

# Development mode
npm run dev

# Production build
npm run build
npm start

Get AI projections for tonight's NBA main slate with explainability

{
  "tool": "get_player_projections",
  "arguments": {
    "sport": "NBA",
    "slate": "main",
    "includeExplanations": true
  }
}

{
  "sport": "NBA",
  "slate": "main",
  "projections": [
    {
      "playerName": "LeBron James",
      "team": "LAL",
      "position": "SF",
      "salary": 9500,
      "projectedPoints": 48.2,
      "floor": 38.6,
      "ceiling": 57.8,
      "confidence": 0.89,
      "value": 5.07,
      "explanation": {
        "topFactors": [
          {
            "factor": "recent_ppg",
            "impact": +6.2,
            "description": "Averaging 32.1 PPG over last 5 games"
          },
          {
            "factor": "vegas_total",
            "impact": +3.1,
            "description": "230.5 Vegas total (high-scoring game expected)"
          }
        ],
        "reasoning": "LeBron is projected above baseline due to elite recent performance and favorable game environment..."
      }
    }
  ]
}

Build me 3 cash game lineups for NBA using the projections you just got

{
  "tool": "optimize_lineup",
  "arguments": {
    "sport": "NBA",
    "salaryCap": 50000,
    "lineupCount": 3,
    "strategy": "cash",
    "projections": [/* from previous call */]
  }
}

{
  "lineups": [
    {
      "rank": 1,
      "players": [
        {"playerName": "Giannis Antetokounmpo", "salary": 11500, "projectedPoints": 54.2},
        {"playerName": "Damian Lillard", "salary": 9000, "projectedPoints": 42.1}
        // ... 6 more players
      ],
      "totalSalary": 49800,
      "projectedPoints": 283.5,
      "riskScore": 22,
      "estimatedOwnership": 18.5
    }
  ]
}

Show me the best odds for tonight's Lakers vs Warriors game

{
  "tool": "get_live_odds",
  "arguments": {
    "sport": "NBA",
    "markets": ["spreads", "totals", "h2h"]
  }
}

┌─────────────────────────────────────────────────┐
│          Claude Desktop / AI Agent              │
└─────────────────┬───────────────────────────────┘
                  │ MCP Protocol
┌─────────────────▼───────────────────────────────┐
│           SportIntel MCP Server                 │
│  ┌──────────────────────────────────────────┐   │
│  │  Tool Registry                           │   │
│  │  - Player Projections                    │   │
│  │  - Lineup Optimizer                      │   │
│  │  - Live Odds                             │   │
│  │  - Explain Recommendation                │   │
│  └──────────────────────────────────────────┘   │
└─────────────────┬───────────────────────────────┘
                  │
      ┌───────────┴───────────┬────────────┐
      │                       │            │
┌─────▼──────┐    ┌──────────▼─┐   ┌─────▼────────┐
│ Odds API   │    │ BallDontLie│   │  XGBoost     │
│ (Betting)  │    │ (NBA Stats)│   │  + SHAP      │
└────────────┘    └────────────┘   └──────────────┘

sportintel-mcp/
├── src/
│   ├── main.ts                    # Entry point
│   ├── mcp-server.ts              # MCP protocol handler
│   ├── tools/                     # MCP tools
│   │   ├── player-projections.ts
│   │   ├── lineup-optimizer.ts
│   │   ├── live-odds.ts
│   │   └── explain-recommendation.ts
│   ├── models/                    # ML models
│   │   ├── xgboost-trainer.ts
│   │   └── explainer.ts
│   ├── integrations/              # Data sources
│   │   ├── odds-api.ts
│   │   └── balldontlie.ts
│   └── types/                     # TypeScript types
├── docs/                          # Documentation
├── tests/                         # Unit & integration tests
└── apify/                         # Apify Actor config

npm run dev          # Development with hot reload
npm run build        # Production build
npm test             # Run tests
npm run train-model  # Train ML models

export class YourTool extends BaseTool {
  definition: MCPTool = {
    name: "your_tool",
    description: "What it does",
    inputSchema: { /* Zod schema */ }
  };

  async execute(args: any) {
    // Your logic here
    return { success: true };
  }
}