sipap-intelligence-mcp

AI-Powered Intelligence MCP Server for SIPAP - News sentiment analysis, injury impact assessment, and weather intelligence using Claude (Bedrock) and OpenWeatherMap.

Overview

This MCP server provides AI-powered intelligence tools that enhance sports predictions with:

• News Sentiment Analysis: Claude-powered analysis of recent news for teams
• Injury Impact Assessment: AI-driven evaluation of injury impact on performance
• Weather Intelligence: Real-time weather forecasts and AI-assessed impact on matches
• Historical Weather Performance: Team performance patterns in specific weather conditions

Architecture

Unlike sipap-data-mcp (database reads only), this MCP server:

• Makes on-demand API calls (OpenWeatherMap, NewsAPI)
• Uses Claude via AWS Bedrock for AI analysis
• Has higher latency (<2s vs <100ms) due to AI processing
• Implements aggressive caching (6h-24h TTL) to minimize API costs

Tools

Weather Tools (3 tools)

1. get_match_weather(match_id: str)

   • Fetches weather forecast for match time and location
   • Source: OpenWeatherMap API
   • Returns: Temperature, precipitation, wind, visibility
   • Cache TTL: 1 hour

2. assess_weather_impact(weather_conditions: dict, match_type: str)

   • AI analysis of weather impact on match outcome
   • Uses: Claude via Bedrock
   • Returns: Impact level, factors, betting implications
   • Cache TTL: 6 hours

3. get_historical_weather_performance(team_id: str, weather_type: str)

   • Analyzes team's historical performance in specific weather
   • Uses: Aurora database + Claude analysis
   • Returns: Performance insights, statistical patterns
   • Cache TTL: 24 hours

News Tools (2 tools)

4. analyze_team_news(team_id: str, days_back: int)

   • Sentiment analysis of recent news headlines
   • Uses: NewsAPI + Claude
   • Returns: Sentiment score, key topics, impact assessment
   • Cache TTL: 6 hours

5. get_injury_reports(team_id: str, severity_filter: str)

   • Injury reports with AI-powered impact assessment
   • Uses: Database + Claude
   • Returns: Injuries with AI-assessed impact scores
   • Cache TTL: 24 hours

Installation

# Install from wheel
pip install sipap_intelligence_mcp-0.1.0-py3-none-any.whl

# Or install in editable mode for development
cd sipap-intelligence-mcp
python -m venv .venv
source .venv/bin/activate
pip install -e '.[dev]'

Requirements

• Python 3.12+
• AWS credentials with Bedrock access
• OpenWeatherMap API key (free tier: 60 calls/min)
• sipap-common >= 0.1.0
• sipap-serverlesshandler-mcp >= 0.1.0

Usage

Direct Tool Usage

from sipap_intelligence_mcp.tools.weather import get_match_weather, assess_weather_impact

# Get weather forecast for match
weather = await get_match_weather(match_id="match-123")
# Returns: {
#     'temperature': 15.2,
#     'precipitation': 'light_rain',
#     'wind_speed': 12.5,
#     'visibility': 8000
# }

# Assess impact on match
impact = await assess_weather_impact(weather, match_type="soccer")
# Returns: {
#     'impact_level': 'medium',
#     'factors': ['Light rain favors defensive play', 'Wind affects long passes'],
#     'betting_implications': 'Consider under 2.5 goals',
#     'confidence': 0.78
# }

MCP Protocol Usage (JSON-RPC 2.0)

from sipap_intelligence_mcp.server import get_mcp_server

# Initialize MCP server
server = get_mcp_server()

# List available tools
request = {
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/list"
}
response = await server.handle_request(request)
# Returns list of 5 tools

# Call a tool
request = {
    "jsonrpc": "2.0",
    "id": 2,
    "method": "tools/call",
    "params": {
        "name": "get_match_weather",
        "arguments": {
            "match_id": "match-123"
        }
    }
}
response = await server.handle_request(request)

Configuration

Environment Variables

# AWS Bedrock (required for AI analysis)
AWS_REGION=us-east-1
BEDROCK_MODEL_ID=anthropic.claude-3-haiku-20240307-v1:0

# OpenWeatherMap API (required for weather)
OPENWEATHER_API_KEY=your_api_key_here

# NewsAPI (optional, for news sentiment)
NEWS_API_KEY=your_api_key_here

# Redis cache (required)
REDIS_ENDPOINT=sipap-dev-cache.cache.amazonaws.com:6379

# Database (required for historical analysis)
DB_ENDPOINT=sipap-dev-aurora.cluster-xxx.us-east-1.rds.amazonaws.com
DB_NAME=sipap_dev
DB_USER=sipap_admin
DB_PASSWORD=stored_in_secrets_manager

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=src/sipap_intelligence_mcp --cov-report=html

# Run type checking
mypy src/sipap_intelligence_mcp --strict

# Run linting
ruff check src/ tests/

# Run all quality gates
pytest && mypy src/sipap_intelligence_mcp --strict && ruff check src/ tests/

Performance

• Latency: <2s average (AI processing overhead)
• Cache Hit Rate: 85%+ target (weather/news change infrequently)
• Cost: ~$10/month (Claude analysis + API calls)
• Rate Limits:
  • OpenWeatherMap: 60 calls/min (free tier)
  • NewsAPI: 100 requests/day (free tier)
  • Claude/Bedrock: Pay-as-you-go (~$0.01 per analysis)

Architecture Patterns

Sentinel Pattern Adoption

• Pattern #9: Structured output enforcement (JSON Schema for AI responses)
• Pattern #19: Lambda warm start optimization (global variables for API clients)
• Pattern #20: Cache-aside with TTL strategy (6h-24h based on volatility)

AI Integration

• Claude Haiku: Fast, cost-effective for simple analyses (<$0.003 per call)
• Claude Sonnet: Complex reasoning for injury impact (<$0.015 per call)
• Prompt Engineering: Sport-specific prompts optimized for accuracy
• Structured Output: Force JSON schema to eliminate parsing errors

Examples

See examples/ directory for:

1. weather_analysis.py - Weather forecast + impact assessment
2. news_sentiment.py - News sentiment analysis for teams
3. injury_impact.py - Injury report with AI assessment
4. mcp_client.py - Full MCP protocol usage example

Development

# Setup development environment
python -m venv .venv
source .venv/bin/activate
pip install -e '.[dev]'

# Run quality gates before committing
pytest && mypy src/sipap_intelligence_mcp --strict && ruff check src/ tests/

License

MIT License - See LICENSE file for details

Support

For issues or questions: charles@sipap.com