投注API服务器

from mcp.client import Client
import asyncio

async def main():
    async with Client("https://server.smithery.ai/@bencousins22/tab-mcp/mcp") as client:
        # Authenticate
        auth = await client.call_tool(
            "tab_oauth_client_credentials",
            {
                "client_id": "your_client_id",
                "client_secret": "your_client_secret"
            }
        )

        # Get next-to-go races
        races = await client.call_tool(
            "racing_get_next_to_go",
            {"access_token": auth["access_token"], "count": 5}
        )

        print(f"Found {len(races['races'])} upcoming races!")

asyncio.run(main())

git clone https://github.com/bencousins22/tab-mcp.git
cd tab-mcp
uv sync  # or: pip install -r requirements.txt

cp .env.example .env
# Edit .env with your Tabcorp API credentials

uv run dev  # Server starts on http://localhost:8081

pytest tests/unit -v
pytest tests/integration -v  # Requires API credentials

graph TB
    subgraph "Client Applications"
        A[Betting Bot]
        B[Form Analyzer]
        C[Odds Comparison]
    end

    subgraph "Tabcorp MCP Server"
        D[FastMCP Server]
        E[OAuth Manager]
        F[Racing Tools]
        G[Sports Tools]
        H[Error Handler]
    end

    subgraph "Tabcorp API"
        I[OAuth Endpoint]
        J[Racing API]
        K[Sports API]
    end

    A --> D
    B --> D
    C --> D
    D --> E
    D --> F
    D --> G
    D --> H
    E --> I
    F --> J
    G --> K

# Analyzes form data, calculates value bets, manages bankroll
# Features: Kelly Criterion, statistical models, bet tracking
# See: TUTORIAL_BETTING_BOT.md

# Comprehensive form analysis with multiple performance metrics
# Features: Statistical scoring, PDF reports, visualizations
# See: TUTORIAL_FORM_ANALYSIS.md

# Multi-sport odds scanning with arbitrage finder
# Features: Real-time scanning, value detection, alerts
# See: TUTORIAL_ODDS_COMPARISON.md

# OAuth Credentials (Required)
TAB_CLIENT_ID=your_client_id
TAB_CLIENT_SECRET=your_client_secret

# Personal Account (Optional - for betting features)
TAB_USERNAME=your_tab_account
TAB_PASSWORD=your_tab_password

# API Configuration
TAB_BASE_URL=https://api.beta.tab.com.au
DEFAULT_JURISDICTION=NSW

ctx.session_config.jurisdiction = "VIC"
ctx.session_config.client_id = "custom_client_id"

pytest tests/ -v

# Unit tests (fast, no API calls)
pytest tests/unit -v

# Integration tests (requires credentials)
pytest tests/integration -v

# Performance tests
pytest tests/performance -v

# Specific category
pytest tests/unit/oauth -v

pytest --cov=src/tab_mcp --cov-report=html
# Open htmlcov/index.html

# Production mode
uv run start

# Or with gunicorn
gunicorn -w 4 -k uvicorn.workers.UvicornWorker src.tab_mcp.server:app

# 1. Fork and clone
git clone https://github.com/YOUR_USERNAME/tab-mcp.git

# 2. Create feature branch
git checkout -b feature/amazing-feature

# 3. Make changes and test
pytest tests/ -v

# 4. Commit and push
git commit -m "Add amazing feature"
git push origin feature/amazing-feature

# 5. Open Pull Request