waifuai/mcp-solana-affiliate

MCP Solana Affiliate Server

This project provides a separate MCP (Model Context Protocol) server for managing the affiliate program associated with the mcp_solana_ico project. It is designed to be completely independent of the main ICO server, handling all aspects of affiliate registration and commission tracking.

Key Features

• Affiliate Registration: Provides an affiliate://register resource that generates unique affiliate IDs and Solana Blink URLs.
• Blink URL Handling: The generated Blink URLs point to an endpoint (/affiliate_buy_tokens) within this server.
• Proxy Functionality: The /affiliate_buy_tokens endpoint acts as a proxy. It receives requests from Blinks, forwards the purchase request to the main ICO server's Action API, and then records the commission.
• Persistent Storage: Affiliate data and commissions are stored persistently in a JSON file (affiliate_data.json).
• Complete Separation: This server has no dependencies on the main mcp_solana_ico server, other than using its Action API URL for constructing purchase transactions. The main ICO server has no knowledge of the affiliate program.
• Health Monitoring: Built-in health check and metrics endpoints for monitoring service status.
• Type Safety: Full type hints and Pydantic models for robust data validation.
• Comprehensive Logging: Structured logging with configurable levels and file output.
• Security: Input validation and security measures throughout the application.

Requirements

• Python 3.11+
• Poetry
• Flask

Installation

1. Clone the repository:

   git clone <repository_url>
   cd mcp_solana_affiliate
   

2. Install dependencies:

   poetry install
   

Configuration

.env file

Create a .env file in the root directory with the following content:

MAIN_SERVER_URL="http://localhost:5000"  # URL of the main mcp_solana_ico server's Action API

Important: MAIN_SERVER_URL must point to the correct location of the main ICO server's Action API.

Usage

1. Start the server:

   poetry run python mcp_solana_affiliate/server.py
   

This will start both the FastMCP server (for affiliate://register) on port 5001 and the Flask app (for handling Blink requests and commission recording) on port 5002.

2. Register Affiliates: Use an MCP client to call the affiliate://register resource. This will return a Solana Blink URL.

3. Token Purchases via Blinks: When a user clicks the Blink URL, it will send a request to the /affiliate_buy_tokens endpoint on this server. This endpoint will:

   • Extract the amount from the request.
   • Forward the purchase request (without any affiliate information) to the main ICO server's Action API (/buy_tokens_action).
   • Receive the serialized Solana transaction from the main server.
   • Record the commission in affiliate_data.json.
   • Return the serialized transaction to the Blink client.

API Documentation

MCP Resources

affiliate://register

Registers a new affiliate and returns a Solana Blink URL.

Response:

"Affiliate registered successfully! Your Solana Blink URL is: solana-action:http://localhost:5000/affiliate_buy_tokens?affiliate_id=123e4567-e89b-12d3-a456-426614174000"

REST Endpoints

POST /affiliate_buy_tokens

Processes token purchases through affiliate links.

Request:

{
  "amount": 1000.0,
  "affiliate_id": "123e4567-e89b-12d3-a456-426614174000"
}

Response (Success):

{
  "transaction": "base64-encoded-transaction"
}

Response (Error):

{
  "error": "Error message"
}

Status Codes:

• 200 - Success
• 400 - Bad Request (validation error)
• 500 - Internal Server Error
• 502 - Bad Gateway (main server error)
• 503 - Service Unavailable (timeout)

POST /record_commission

Manually records a commission for an affiliate.

Request:

{
  "affiliate_id": "123e4567-e89b-12d3-a456-426614174000",
  "ico_id": "main_ico",
  "amount": 1000.0,
  "commission": 50.0,
  "client_ip": "192.168.1.1"
}

Response (Success):

{
  "message": "Commission recorded successfully"
}

Response (Error):

{
  "error": "Error message"
}

GET /health

Health check endpoint for monitoring.

Response:

{
  "status": "healthy",
  "timestamp": 1234567890,
  "service": "mcp-solana-affiliate",
  "version": "1.0.0",
  "checks": {
    "affiliate_data": "healthy",
    "main_server": "healthy",
    "database": "healthy"
  }
}

GET /metrics

Metrics endpoint for monitoring service statistics.

Response:

{
  "total_affiliates": 10,
  "total_commissions": 25,
  "total_commission_amount": 1250.0,
  "timestamp": 1234567890
}

GET /cache/stats

Cache statistics endpoint for monitoring cache performance.

Response:

{
  "affiliate_cache": {
    "total_items": 5,
    "expired_items": 1,
    "active_items": 4,
    "hit_count": 150,
    "miss_count": 25
  },
  "metrics_cache": { ... },
  "health_cache": { ... }
}

POST /cache/clear

Clear all cache data.

Response:

{
  "message": "All caches cleared successfully"
}

POST /cache/cleanup

Remove expired items from cache.

Response:

{
  "message": "Cache cleanup completed",
  "items_removed": 3
}

Configuration

The application uses Pydantic models for configuration management. Configuration can be set via environment variables:

Environment Variables

Data Models

The application uses the following data models:

CommissionRecord

{
  "ico_id": "main_ico",
  "amount": 1000.0,
  "commission": 50.0,
  "client_ip": "192.168.1.1",
  "timestamp": 1234567890
}

AffiliateData

{
  "affiliate_id": "123e4567-e89b-12d3-a456-426614174000",
  "commissions": [CommissionRecord],
  "created_at": 1234567890,
  "last_updated": 1234567890
}

Project Structure

mcp_solana_affiliate/
├── affiliates.py      # Core affiliate logic (generation, storage, commission recording)
├── server.py           # Main server code (FastMCP resource and Flask app)
├── config.py           # Configuration management with Pydantic models
├── models.py           # Data models using Pydantic
├── services.py         # Service layer for business logic
├── __init__.py
.env                    # Environment variables
.gitignore
pyproject.toml          # Poetry configuration
README.md               # This file

Architecture

The application follows a layered architecture:

• Presentation Layer: Flask endpoints handling HTTP requests/responses
• Service Layer: Business logic in services.py (AffiliateService, TransactionService, etc.)
• Data Layer: Data access and persistence in affiliates.py
• Configuration Layer: Centralized configuration management in config.py
• Model Layer: Data validation and serialization in models.py

Key Components

• AffiliateService: Handles affiliate registration and data retrieval
• TransactionService: Processes token purchases and commission recording
• HealthService: Provides health check and monitoring functionality
• MetricsService: Collects and exposes service metrics
• Configuration Management: Environment-based configuration with Pydantic validation
• Error Handling: Comprehensive error handling with appropriate HTTP status codes
• Logging: Structured logging with configurable levels and outputs

Development

Setup

1. Clone the repository:

   git clone <repository_url>
   cd mcp_solana_affiliate
   

2. Install dependencies:

   poetry install
   

3. Set up environment:

   cp .env.example .env
   # Edit .env with your configuration
   

Running Tests

# Run all tests
poetry run pytest

# Run with coverage
poetry run pytest --cov=mcp_solana_affiliate

# Run specific test file
poetry run pytest tests/test_server.py

Running the Application

# Development mode
poetry run python -m mcp_solana_affiliate.server

# Production mode (with gunicorn)
poetry run gunicorn -w 4 -b 0.0.0.0:5002 mcp_solana_affiliate.server:app

Code Quality

# Format code
poetry run black mcp_solana_affiliate tests

# Lint code
poetry run ruff check mcp_solana_affiliate tests

# Type checking
poetry run mypy mcp_solana_affiliate

Monitoring

Health Checks

The service provides health check endpoints:

• GET /health - Overall health status
• GET /metrics - Service metrics and statistics

Logging

Logs are output to:

• Console (INFO level by default)
• Optional log file (if LOG_FILE environment variable is set)

Log levels can be configured via the LOG_LEVEL environment variable.

Security

• Input validation using Pydantic models
• CORS configuration for cross-origin requests
• Environment variable validation
• Rate limiting support (configurable)
• IP-based affiliate limits (configurable)

Performance

Caching Strategy

The application implements a multi-tier caching strategy:

• Affiliate Cache: 5-minute TTL for affiliate data to reduce database reads
• Metrics Cache: 1-minute TTL for metrics data
• Health Cache: 30-second TTL for health check data

Performance Optimizations

• Efficient JSON file storage with in-memory caching
• Configurable HTTP timeouts and retry logic
• Connection pooling for external requests
• Thread-safe caching with automatic cleanup
• Metrics collection for monitoring bottlenecks
• Lazy loading of configuration

Cache Management

Cache can be managed via API endpoints:

• Monitor cache performance: GET /cache/stats
• Clear all caches: POST /cache/clear
• Cleanup expired items: POST /cache/cleanup

Monitoring

Performance metrics are exposed via:

• GET /metrics - Service usage statistics
• GET /health - Health status with dependency checks
• GET /cache/stats - Cache performance metrics

Future Considerations

• Database Storage: Replace JSON file storage with a more robust database (PostgreSQL, MongoDB)
• Caching: Implement Redis caching for frequently accessed data
• Authentication: Add API key authentication for endpoints
• Rate Limiting: Implement more sophisticated rate limiting
• Monitoring: Integrate with Prometheus/Grafana for advanced monitoring
• Scaling: Add support for horizontal scaling with load balancing

Key Files

• affiliates.py:

  • generate_affiliate_id(): Generates a unique affiliate ID using uuid.uuid4().
  • load_affiliate_data(): Loads affiliate data from affiliate_data.json.
  • save_affiliate_data(): Saves affiliate data to affiliate_data.json.
  • store_affiliate_data(): Stores affiliate data.
  • get_affiliate_data(): Retrieves affiliate data.
  • record_commission(): Records a commission for an affiliate.

• server.py:

  • mcp = FastMCP(name="Solana Affiliate Server"): Initializes the FastMCP server.
  • @mcp.resource("affiliate://register"): The FastMCP resource for registering affiliates.
  • app = Flask(__name__): Initializes the Flask application.
  • @app.route('/affiliate_buy_tokens', methods=['POST', 'OPTIONS']): The Flask endpoint that handles Blink requests, forwards them to the main server, and records commissions.
  • @app.route('/record_commission', methods=['POST']): A Flask endpoint for manually recording commissions (not strictly required, but potentially useful).

Future Considerations

• Database Storage: Replace the JSON file storage with a more robust database (e.g., PostgreSQL, SQLite).
• Error Handling: Implement more comprehensive error handling and logging.
• Security: Implement security measures, such as authentication and authorization, especially for the /record_commission endpoint.
• Scalability: Consider using a message queue (e.g., RabbitMQ, Kafka) for handling commission recording asynchronously, improving scalability.
• Testing: Add unit and integration tests.