MCP Boilerplate

# MCP Example (iddv) **Repository**: https://github.com/iddv/mcp-example ## Overview A reference implementation of the Model Context Protocol (MCP) enabling seamless tool calling between LLMs and applications. Features client/server architecture with HTTP APIs, local CLI execution, and AWS Bedrock integration in a production-ready, extensible framework. Powered by FastMCP 2.0 with sophisticated examples including a living Minesweeper game. ## Tech Stack - **Python 3.10+** - Modern Python features - **FastMCP 2.0** - Enterprise-grade MCP framework - **Poetry** - Dependency management and packaging - **pytest** - Testing framework - **black, isort, ruff, mypy** - Code quality tools - **AWS Bedrock** - Cloud AI integration ## Project Structure ``` examples/ ├── servers/ # Multiple server implementations ├── clients/ # Client integration examples ├── configs/ # Configuration examples ├── tests/ # Comprehensive testing suite └── docs/ # Documentation and guides template_server.py # Clean starting canvas with boilerplate ``` ## Key Features - ✅ **FastMCP 2.0 Powered** - Enterprise-grade framework - ✅ **Multiple Transport Protocols** - stdio, HTTP, WebSocket support - ✅ **OAuth 2.0 Authentication** - Production-ready security - ✅ **Real-Time Data Streaming** - Live data integration - ✅ **Session Management** - Stateful client connections - ✅ **AWS Bedrock Integration** - Cloud AI platform support - ✅ **Minesweeper Game Example** - AI logical reasoning demonstration - ✅ **AI Agent-Friendly Templates** - Optimized for AI development ## FastMCP 2.0 Enterprise Features - **Multi-Protocol Transport**: stdio, HTTP, WebSocket protocols - **Authentication System**: OAuth 2.0 integration - **Session Management**: Persistent client state - **Real-Time Streaming**: Live data and event streaming - **Resource & Tool Definitions**: Flexible API patterns - **Production Security**: Enterprise-grade authentication ## Minesweeper Implementation - **AI-Powered Gameplay**: Strategic AI reasoning demonstration - **Probabilistic Analysis**: Advanced board analysis algorithms - **Multiple Difficulty Levels**: Beginner, intermediate, expert modes - **Interactive Hints**: Strategy guidance and hints system - **Living Game Example**: Real-time AI logical reasoning showcase ## Implementation Patterns - **Decorator-Based Definitions**: Clean tool and resource registration - **Async Programming**: Modern Python async/await patterns - **Modular Design**: Extensible server architecture - **Comprehensive Error Handling**: Production-ready error management - **Type Safety**: Full mypy type checking ## Production-Ready Features - **Comprehensive Testing**: pytest-based test infrastructure - **Code Quality**: black, isort, ruff, mypy integration - **Documentation**: Extensive guides and examples - **Template System**: `template_server.py` for quick starts - **AI Agent Optimization**: Templates designed for AI development ## AWS Integration - **Bedrock Integration**: Direct AWS AI platform connectivity - **Cloud Deployment**: Production cloud deployment patterns - **Scalable Architecture**: Enterprise-grade scalability ## Unique Approaches - **Living Game Example**: Interactive Minesweeper demonstrating AI reasoning - **AI Agent Friendly**: Optimized templates for AI development workflows - **Comprehensive Examples**: Multiple server implementations for learning - **Enterprise Focus**: Production-ready patterns and practices ## Template Philosophy > "This repository is optimized for AI agents to build MCP servers! template_server.py - Clean starting canvas with all boilerplate" ## Recommended Use Cases - Enterprise MCP server development - AI-powered interactive applications - Learning advanced MCP patterns - Production deployments requiring authentication - Complex, stateful AI integrations - AWS Bedrock-based AI applications ## Analysis This repository represents the most comprehensive and enterprise-ready approach to MCP development. The FastMCP 2.0 integration, OAuth authentication, real-time streaming, and AWS Bedrock connectivity make it ideal for production applications. The Minesweeper example brilliantly demonstrates AI logical reasoning capabilities, while the AI agent-optimized templates show forward-thinking approach to AI development workflows. The extensive testing, documentation, and code quality tools make it suitable for large-scale, mission-critical applications.