Traefik MCP Server

# Traefik MCP Server - Project Planning ## Project Overview This project aims to create a Model Context Protocol (MCP) server that enables AI assistants to interact with Traefik, a modern cloud-native reverse proxy and load balancer. The MCP server will act as a bridge between AI assistants and Traefik instances, allowing natural language interaction with Traefik's configuration, monitoring, and management capabilities. ## What is Traefik? Traefik is an open-source reverse proxy and load balancer designed for microservices and containerized applications. Key features include: - Dynamic configuration through Docker labels, Kubernetes annotations, or file providers - Automatic service discovery - Built-in Let's Encrypt SSL certificate management - HTTP/HTTPS, TCP, UDP protocol support - API and dashboard for monitoring and management - Middleware support for request transformation ## What is MCP (Model Context Protocol)? MCP is an open standard created by Anthropic for connecting AI assistants to external systems and data sources. It provides a standardized way for AI applications to: - Access data from various sources (databases, APIs, file systems) - Execute tools and commands - Provide context-aware responses - Maintain secure, controlled interactions with external systems ## Project Goals ### Primary Objectives 1. **Read Operations**: Query Traefik configuration, status, and metrics 2. **Write Operations**: Modify Traefik configuration dynamically (routers, services, middlewares) 3. **Monitoring**: Access Traefik metrics, health checks, and logs 4. **Management**: Perform administrative tasks (restart services, reload configuration) ### Use Cases - AI-assisted Traefik configuration management - Natural language queries about routing rules and service status - Automated troubleshooting and diagnostics - Configuration generation based on requirements - Real-time monitoring and health checks ## Technical Architecture ### Core Components 1. **MCP Server (Python)** - Built using the official MCP Python SDK - Exposes tools, resources, and prompts to AI clients - Handles authentication to single Traefik instance - Manages single persistent connection to Traefik 2. **Traefik Integration Layer** - Communicates with Traefik API (REST endpoints) - Parses and validates configuration files (YAML/TOML) - Interacts with Traefik providers (Docker, Kubernetes, File) - Handles dynamic configuration updates 3. **Data Models** - Traefik configuration schemas (routers, services, middlewares, entrypoints) - Response formatters for AI consumption - Validation schemas for configuration changes ### Technology Stack **Core Framework:** - Python 3.10+ - MCP Python SDK (`mcp`) - AsyncIO for asynchronous operations **Traefik Integration:** - `httpx` for async HTTP requests to Traefik API - `pyyaml` for YAML configuration parsing - `toml` for TOML configuration parsing - `docker` library for Docker provider integration (optional) - `kubernetes` library for K8s integration (optional) **Development Tools:** - `uv` for dependency management and project setup - `pytest` for testing - `pytest-asyncio` for async test support - `black` and `ruff` for code formatting and linting - Type hints with `mypy` **Data Validation:** - `pydantic` for data validation and serialization **Optional Features:** - `python-dotenv` for environment configuration - `structlog` for structured logging ## Project Structure ``` traefik-mcp-server/ ├── src/ │ └── traefik_mcp/ │ ├── __init__.py │ ├── server.py # Main MCP server implementation │ ├── tools/ # MCP tools (commands AI can execute) │ │ ├── __init__.py │ │ ├── config_tools.py # Configuration management tools │ │ ├── query_tools.py # Query and inspection tools │ │ └── monitoring_tools.py # Monitoring and metrics tools │ ├── resources/ # MCP resources (data AI can access) │ │ ├── __init__.py │ │ └── traefik_data.py # Traefik data resources │ ├── prompts/ # MCP prompts (templates) │ │ ├── __init__.py │ │ └── traefik_prompts.py │ ├── traefik/ # Traefik integration │ │ ├── __init__.py │ │ ├── api_client.py # Traefik API client │ │ ├── config_parser.py # Configuration parsing │ │ └── models.py # Data models │ └── utils/ │ ├── __init__.py │ └── helpers.py ├── tests/ │ ├── __init__.py │ ├── test_tools.py │ ├── test_api_client.py │ ├── test_config_parser.py │ └── fixtures/ │ └── sample_configs/ ├── docs/ │ ├── setup.md │ ├── usage.md │ └── api.md ├── examples/ │ ├── basic_query.py │ ├── config_update.py │ └── ai_client_configs/ │ ├── claude_desktop.json │ └── cline.json ├── pyproject.toml ├── README.md ├── PLANNING.md ├── TASKS.md └── .env.example ``` ## MCP Server Capabilities ### Tools (Actions AI Can Perform) 1. **Configuration Tools** - `get_router`: Retrieve specific router configuration - `list_routers`: List all HTTP/TCP routers - `create_router`: Create a new router - `update_router`: Modify existing router - `delete_router`: Remove router - `get_service`: Retrieve specific service configuration - `list_services`: List all services - `create_service`: Create a new service - `update_service`: Modify existing service - `get_middleware`: Retrieve middleware configuration - `list_middlewares`: List all middlewares - `create_middleware`: Create new middleware 2. **Query Tools** - `get_overview`: Get complete Traefik overview - `search_routes`: Find routes matching criteria - `get_entrypoints`: List all entrypoints - `get_providers`: List active providers 3. **Monitoring Tools** - `get_health`: Overall health check - `get_metrics`: Retrieve Traefik metrics (if Prometheus enabled) - `get_version`: Get Traefik version info - `check_service_health`: Check specific service health 4. **Validation Tools** - `validate_config`: Validate configuration syntax - `dry_run`: Test configuration changes without applying ### Resources (Data AI Can Access) 1. **current-config**: Complete Traefik configuration snapshot 2. **routers-list**: Catalog of all routers with metadata 3. **services-list**: Catalog of all services 4. **middlewares-list**: Catalog of all middlewares 5. **health-status**: Real-time health status ### Prompts (Templates for Common Tasks) 1. **create-route**: Guide for setting up a new route 2. **troubleshoot-503**: Diagnostic workflow for 503 errors 3. **security-check**: Review configuration for security best practices 4. **ssl-setup**: Configure SSL/TLS for a service ## Configuration The MCP server will support configuration through environment variables for a single Traefik instance: ```bash # Required: Traefik API URL TRAEFIK_API_URL=http://localhost:8080 # Optional: API Key (if Traefik API requires authentication) TRAEFIK_API_KEY=your-api-key-here # Optional: MCP Server Configuration MCP_LOG_LEVEL=INFO ``` **Note**: The server is designed to manage exactly one Traefik instance. The connection details are specified when the MCP server starts and remain constant for the lifetime of the server process. ## Security Considerations 1. **Authentication** - Support for Traefik API Basic Authentication - Secure credential storage via environment variables - Optional: API key authentication 2. **Authorization** - Read-only mode vs. read-write mode configuration - Validation before any write operations - Audit logging for all configuration changes 3. **Validation** - Input validation for all configuration changes - Schema validation against Traefik configuration spec - Dry-run capability to test changes safely 4. **Network Security** - HTTPS for Traefik API communication - Certificate verification options - Timeout configurations to prevent hanging requests ## Development Phases ### Phase 1: Foundation (MVP) **Goal**: Basic read-only MCP server that can query Traefik - Set up project structure with uv - Implement Traefik API client for read operations - Create basic MCP server with core tools: - `list_routers` - `list_services` - `get_overview` - Implement basic resources (current-config) - Write unit tests for API client - Create README and setup documentation **Success Criteria**: Can query Traefik configuration via an AI client ### Phase 2: Configuration Management **Goal**: Enable write operations with safety checks - Implement configuration modification tools - Add validation layer - Implement file-based provider support - Add backup/rollback functionality - Create comprehensive error handling - Add integration tests **Success Criteria**: Can safely modify Traefik configuration through AI ### Phase 3: Advanced Features **Goal**: Enhanced monitoring and multi-provider support - Docker provider integration - Kubernetes provider integration (optional) - Metrics and monitoring tools - Advanced query capabilities (search, filter) - Health check monitoring - Prometheus metrics integration **Success Criteria**: Full-featured management across multiple providers ### Phase 4: Polish & Production Ready **Goal**: Production-ready release - Comprehensive documentation - Example configurations and use cases - Performance optimization - Error message improvements - CI/CD setup - Package for PyPI distribution **Success Criteria**: Ready for community use ## Success Metrics 1. **Functionality**: All core CRUD operations working reliably 2. **Reliability**: 95%+ success rate for API calls 3. **Performance**: <1s response time for typical operations 4. **Usability**: Clear, helpful error messages and AI responses 5. **Documentation**: Setup guide allows new users to start in <10 minutes ## Potential Challenges & Solutions | Challenge | Solution | |-----------|----------| | API version compatibility | Support multiple Traefik versions, detect version on connect | | Real-time configuration changes | Implement polling or webhook support for updates | | Complex configuration validation | Use Pydantic models matching Traefik schemas | | Error handling from Traefik API | Wrap errors with helpful context for AI interpretation | | State consistency | Implement caching with TTL, refresh mechanisms | | Connection failures | Retry logic with exponential backoff, clear error messages | ## Future Enhancements 1. **Configuration Templates**: Pre-built templates for common scenarios (WordPress, API gateway, etc.) 2. **Migration Tools**: Help migrate from Nginx/Apache configurations 3. **Best Practices Checker**: Automated security and performance recommendations 4. **Visualization**: Generate configuration diagrams 5. **GitOps Integration**: Commit configuration changes to Git 6. **Backup Management**: Automated backup and restore workflows 7. **Multi-Instance Support**: Manage multiple Traefik instances (separate MCP servers or instance switching) 8. **Plugin Architecture**: Allow custom tools and extensions ## Testing Strategy 1. **Unit Tests**: Test individual components (API client, parsers, tools) 2. **Integration Tests**: Test MCP server with mock Traefik API 3. **E2E Tests**: Test with real Traefik instance (Docker-based) 4. **Manual Testing**: Test with AI client for UX validation ## Documentation Plan 1. **README.md**: Quick start, installation, basic usage 2. **docs/setup.md**: Detailed setup for different environments 3. **docs/usage.md**: Common tasks and examples 4. **docs/api.md**: Tool and resource reference 5. **docs/development.md**: Contributing guide 6. **examples/**: Code examples for different use cases ## Resources & References - [Traefik Documentation](https://doc.traefik.io/traefik/) - [Traefik API Reference](https://doc.traefik.io/traefik/operations/api/) - [MCP Documentation](https://modelcontextprotocol.io/) - [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk) - [MCP Specification](https://spec.modelcontextprotocol.io/)