MCP Server

The AltSportsLeagues MCP Server gives AI agents native access to league intelligence, discovery, and valuation capabilities through the Model Context Protocol.

What is MCP?

The Model Context Protocol (MCP) is an open standard that lets AI assistants like Claude, ChatGPT, and Cursor connect to external data sources and tools through a standardized interface.

With the AltSportsLeagues MCP Server, AI agents can:

• 🔍 Search and discover alternative sports leagues
• 📊 Evaluate league quality and partnership potential
• 🏆 Access tier classifications and scoring data
• 📈 Analyze league data and trends
• 🤝 Assist with partnership decisions

Quick Start

Installation

pip install altsportsleagues-mcp

Configuration

Add to your MCP settings file (e.g., Claude Desktop config):

{
  "mcpServers": {
    "altsportsleagues": {
      "command": "python",
      "args": ["-m", "altsportsleagues_mcp"],
      "env": {
        "ASL_API_KEY": "your_api_key_here"
      }
    }
  }
}

Usage in Claude Desktop

Once configured, Claude can automatically use league intelligence tools:

You: "Find professional pickleball leagues in North America"

Claude: Uses search_leagues tool

"I found 3 professional pickleball leagues in North America:

1. Major League Pickleball (Tier 2, US)
2. Professional Pickleball Association (Tier 2, US)
3. Canadian Pickleball League (Tier 3, Canada)"

Available Tools

The MCP server exposes these tools to AI agents:

search_leagues

Intelligent league search and discovery

Parameters:

• query (string): Search query
• min_confidence (number, optional): Minimum confidence score (0-1)
• limit (number, optional): Maximum results

Example:

// AI agent automatically calls:
search_leagues({
  query: "professional ultimate frisbee leagues",
  min_confidence: 0.7,
  limit: 5
})

get_league_valuation

Get comprehensive quality scoring for a league

Parameters:

• league_id (string): League identifier

Example:

get_league_valuation({
  league_id: "league_001"
})

list_leagues_by_tier

Browse leagues filtered by tier classification

Parameters:

• tier (string): Tier classification (tier-1, tier-2, tier-3, tier-4)
• sport (string, optional): Filter by sport
• limit (number, optional): Maximum results

Example:

list_leagues_by_tier({
  tier: "tier-2",
  sport: "Soccer",
  limit: 10
})

evaluate_partnership

Assess partnership potential between a league and sportsbook

Parameters:

• league_id (string): League identifier
• requirements (object): Sportsbook requirements

Example:

evaluate_partnership({
  league_id: "league_001",
  requirements: {
    min_tier: "tier-2",
    real_time_data: true,
    min_api_quality: 80
  }
})

Integration Examples

Claude Desktop

1. Install the MCP server:

pip install altsportsleagues-mcp

2. Add to Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "altsportsleagues": {
      "command": "python",
      "args": ["-m", "altsportsleagues_mcp"],
      "env": {
        "ASL_API_KEY": "demo-0001"
      }
    }
  }
}

3. Restart Claude Desktop and start asking about leagues!

Cursor IDE

1. Install the MCP server
2. Configure in Cursor settings
3. Ask Cursor to research leagues while coding

Custom MCP Client

from mcp import ClientSession, StdioServerParameters
import asyncio

async def main():
    server_params = StdioServerParameters(
        command="python",
        args=["-m", "altsportsleagues_mcp"],
        env={"ASL_API_KEY": "demo-0001"}
    )
    
    async with ClientSession(server_params) as session:
        # List available tools
        tools = await session.list_tools()
        print(f"Available tools: {tools}")
        
        # Call a tool
        result = await session.call_tool("search_leagues", {
            "query": "professional cricket leagues",
            "limit": 5
        })
        print(result)

asyncio.run(main())

Use Cases

For Sportsbook Analysts

• Market Research: "Find tier-2 basketball leagues in Asia"
• Quality Assessment: "Compare data quality of these three leagues"
• Partnership Planning: "Which pickleball leagues should we prioritize?"

For League Business Development

• Competitive Analysis: "Find leagues similar to ours"
• Improvement Guidance: "What do we need to reach tier-2?"
• Market Positioning: "How does our league compare to others in our sport?"

For Developers

• Integration Planning: "Evaluate API quality for league X"
• Data Mapping: "What data fields does this league provide?"
• Technical Due Diligence: "Assess integration effort for these leagues"

Configuration

Environment Variables

# Required
ASL_API_KEY=your_api_key

# Optional
ASL_BASE_URL=https://api.altsportsleagues.ai/v1
ASL_TIMEOUT=30
ASL_DEBUG=false

Config File

Alternatively, use a config file (~/.altsportsleagues/config.json):

{
  "api_key": "your_api_key",
  "base_url": "https://api.altsportsleagues.ai/v1",
  "timeout": 30,
  "debug": false
}

Troubleshooting

Server Not Found

Make sure the package is installed and accessible:

python -m altsportsleagues_mcp --version

Authentication Errors

Verify your API key is set correctly:

echo $ASL_API_KEY

Tool Calls Failing

Enable debug logging:

ASL_DEBUG=true python -m altsportsleagues_mcp

Next Steps

Resources

• MCP Specification: modelcontextprotocol.io
• Claude Desktop: claude.ai/download
• GitHub: github.com/altsportsleagues/altsportsleagues-mcp
• PyPI: pypi.org/project/altsportsleagues-mcp