拳击智能分析平台

# Clone repository
git clone https://github.com/yourusername/mcp-orchestrator
cd mcp-orchestrator

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys:
# - ANTHROPIC_API_KEY (required)
# - ODDS_API_KEY (optional)
# - NEWS_API_KEY (optional)
# - REDDIT_CLIENT_ID, REDDIT_CLIENT_SECRET (optional)

# Initialize database
python scripts/init_boxing_db.py

# Run the application
streamlit run frontend/app.py

# Simple fighter lookup
"Tell me about Tyson Fury's boxing record"

# Head-to-head comparison
"Compare Tyson Fury vs Oleksandr Usyk statistically. Who has the advantage?"

# Career trajectory analysis (Advanced)
"Is Tyson Fury improving or declining? Analyze his recent career trajectory"

# Common opponent comparison (Advanced)
"Compare Fury and Usyk's performance against fighters they've both faced"

# Title fight performance (Advanced)
"Does Canelo Alvarez perform better in championship fights vs regular fights?"

# Multi-source analysis
"What are the betting odds for upcoming heavyweight fights and where's the best value?"

# Community sentiment
"What is the boxing community saying about Canelo Alvarez?"

# Complex synthesis
"Analyze Fury's career trajectory, compare it to betting odds for his next fight, and tell me if the odds reflect his current form"

mcp-orchestrator/
├── frontend/
│   ├── app.py                      # Streamlit interface
│   └── assets/                     # UI assets
├── mcp_servers/
│   ├── boxing_data.py              # Core analytics server
│   ├── boxing_prediction.py        # Advanced prediction tools
│   ├── boxing_odds.py              # Betting server
│   ├── boxing_news.py              # News server
│   └── reddit.py                   # Social server
├── data/
│   └── boxing_data.db              # Fighter database (SQLite)
├── observability/
│   └── monitoring.py               # LangSmith integration
├── requirements.txt                # Python dependencies
├── start.sh                        # Railway start script
├── nixpacks.toml                   # Python version config
└── railway.json                    # Railway deployment config

# Add Railway config files
cp railway.json .
cp nixpacks.toml .
cp start.sh .
chmod +x start.sh

# Commit and push
git add .
git commit -m "Add Railway deployment config"
git push

# Deploy on Railway
# 1. Go to railway.app
# 2. New Project → Deploy from GitHub
# 3. Select repository
# 4. Add environment variables
# 5. Generate domain

ANTHROPIC_API_KEY=sk-ant-api03-xxxxx
ODDS_API_KEY=xxxxx (optional)
NEWS_API_KEY=xxxxx (optional)
REDDIT_CLIENT_ID=xxxxx (optional)
REDDIT_CLIENT_SECRET=xxxxx (optional)
REDDIT_USER_AGENT=boxonomics_v1 (optional)

# Run with hot reload
streamlit run frontend/app.py --server.runOnSave=true

# Test individual MCP servers
python mcp_servers/boxing_data.py

# Test advanced prediction tools
python mcp_servers/boxing_prediction.py

# View logs
tail -f logs/app.log

{
  "fighter": "Tyson Fury",
  "current_trajectory": {
    "trend": "Stable",
    "trend_strength": "Consistent",
    "recent_win_rate": 85.0,
    "change_from_early_career": -2.3
  },
  "career_phases": {
    "early_career": {"wins": 12, "win_rate": 1.0},
    "mid_career": {"wins": 10, "win_rate": 0.83},
    "late_career": {"wins": 8, "win_rate": 0.88}
  }
}

{
  "fighter1": "Tyson Fury",
  "fighter2": "Deontay Wilder",
  "common_opponents_count": 3,
  "score": {
    "Tyson Fury": 2.5,
    "Deontay Wilder": 0.5
  },
  "overall_advantage": "Tyson Fury",
  "confidence": 0.8
}

# Run all tests
pytest

# Test individual server
python mcp_servers/boxing_data.py

# Test advanced predictions
python mcp_servers/boxing_prediction.py

# Test with sample queries
python cli.py test