MCP Integration for Sportsbooks

{
  "mcpServers": {
    "altsportsdata-sportsbook": {
      "command": "npx",
      "args": ["-y", "@altsportsdata/mcp-server"],
      "env": {
        "API_KEY": "your-sportsbook-api-key",
        "ROLE": "sportsbook",
        "MARKET_ACCESS": "true"
      }
    }
  }
}

{
  "name": "get_live_match",
  "arguments": {
    "match_id": "epl-2024-001",
    "include_live_stats": true,
    "include_markets": true
  }
}

{
  "match_id": "epl-2024-001",
  "status": "live",
  "minute": 67,
  "home_team": {
    "name": "Manchester United",
    "score": 2,
    "shots": 12,
    "possession": 58
  },
  "away_team": {
    "name": "Liverpool",
    "score": 1,
    "shots": 9,
    "possession": 42
  },
  "live_markets": {
    "moneyline": {
      "home": 1.85,
      "draw": 3.40,
      "away": 4.20
    },
    "total_goals": {
      "over_3.5": 2.10,
      "under_3.5": 1.75
    }
  }
}

{
  "name": "get_market_intelligence",
  "arguments": {
    "sport": "basketball",
    "market_type": "spread",
    "timeframe": "24h"
  }
}

{
  "name": "get_player_props",
  "arguments": {
    "player_id": "lebron-james",
    "stat_type": "points",
    "season": "2024-25"
  }
}

{
  "name": "monitor_exposure",
  "arguments": {
    "sport": "soccer",
    "league": "epl",
    "exposure_type": "liability"
  }
}

{
  "name": "compare_odds",
  "arguments": {
    "match_id": "epl-2024-001",
    "market_types": ["moneyline", "spread", "total"]
  }
}

User: "Set up markets for tonight's NBA games"

AI Agent (using MCP):
1. Fetches schedule with get_schedule
2. Retrieves team stats with get_team_stats
3. Analyzes historical matchups with get_h2h_stats
4. Suggests opening lines based on data
5. Provides market recommendations

Result: Complete market setup with recommended odds

User: "A key player just got injured in the Liverpool match, adjust odds"

AI Agent (using MCP):
1. Identifies match with get_live_match
2. Retrieves player impact data with get_player_impact
3. Calculates odds adjustment with calculate_adjustment
4. Suggests new odds across all markets
5. Updates exposure calculations

Result: Real-time odds adjustments with risk analysis

User: "Create player props for LeBron James tonight"

AI Agent (using MCP):
1. Gets player stats with get_player_props
2. Analyzes opponent defense with get_team_defense_stats
3. Reviews recent performance with get_player_recent_form
4. Suggests prop lines and odds
5. Calculates market limits

Result: Complete player prop offerings with suggested limits

User: "Show me our biggest liabilities for this weekend"

AI Agent (using MCP):
1. Monitors exposure with monitor_exposure
2. Identifies high-risk positions with analyze_risk
3. Suggests hedging opportunities with find_hedges
4. Provides limit recommendations with suggest_limits

Result: Comprehensive risk report with actionable insights

"Analyze betting patterns for tonight's Champions League matches"
"Compare our odds to market averages for Premier League games"
"Show sharp money movement in NBA spreads today"

"What's our exposure on Manchester United to win the league?"
"Identify correlated risk across parlay bets this weekend"
"Calculate optimal limits for live betting on NFL games"

"Create props for all NBA games tonight"
"What's Mahomes' passing yards trend against zone defense?"
"Suggest player prop limits based on recent form"

"Monitor all live soccer matches and alert on significant events"
"Adjust odds for the Lakers game based on current score"
"Suspend markets where home team is dominating"

import { MCPClient } from '@modelcontextprotocol/sdk';
 
const sportsbookClient = new MCPClient({
  serverUrl: 'http://localhost:3000/mcp',
  apiKey: process.env.SPORTSBOOK_API_KEY,
  role: 'sportsbook'
});
 
// Get live match data
const liveMatch = await sportsbookClient.call('get_live_match', {
  match_id: 'epl-2024-001',
  include_markets: true
});
 
// Monitor risk exposure
const exposure = await sportsbookClient.call('monitor_exposure', {
  sport: 'soccer',
  league: 'epl'
});

from altsportsdata_mcp import MCPClient
 
client = MCPClient(
    api_key=os.getenv('SPORTSBOOK_API_KEY'),
    role='sportsbook'
)
 
# Get player props
props = client.call_tool('get_player_props', {
    'player_id': 'lebron-james',
    'stat_type': 'points'
})
 
# Compare odds
comparison = client.call_tool('compare_odds', {
    'match_id': 'nba-2024-001',
    'market_types': ['spread', 'total']
})

try {
  const match = await client.call('get_live_match', { match_id: 'epl-001' });
} catch (error) {
  if (error.code === 'MATCH_NOT_FOUND') {
    // Handle missing match
  } else if (error.code === 'RATE_LIMIT_EXCEEDED') {
    // Implement backoff strategy
  } else if (error.code === 'MARKET_SUSPENDED') {
    // Handle suspended market
  }
}

{
  "name": "create_custom_market",
  "arguments": {
    "match_id": "epl-2024-001",
    "market_type": "custom",
    "definition": {
      "name": "Total Corners in Second Half",
      "type": "over_under",
      "line": 5.5
    }
  }
}

{
  "name": "suggest_hedges",
  "arguments": {
    "exposure_threshold": 100000,
    "sport": "soccer",
    "market_types": ["moneyline", "spread"]
  }
}

{
  "name": "predict_outcome",
  "arguments": {
    "match_id": "nba-2024-001",
    "model_type": "ensemble",
    "factors": ["recent_form", "h2h", "injuries"]
  }
}