Model Context Protocol (MCP) Examples

A comprehensive repository showcasing examples of implementing Model Context Protocol (MCP) features. This repository serves as a resource for developers to understand and build upon MCP implementations using Python.

What is Model Context Protocol (MCP)?

The Model Context Protocol is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). It defines a consistent interface for tools, resources, and prompts, enabling seamless integration between LLMs and various data sources or capabilities.

Repository Structure

This repository is organized into several key sections:

Patterns

• Composability

  • Client-orchestrated workflows
  • Proxy server implementations
  • Server-to-server communication

• Orchestration

  • Centralized orchestrators
  • Dynamic workflow orchestration
  • Event-driven orchestration

Prompts

• Conditional workflows
• Dynamic arguments
• Orchestrated pipelines
• Slash commands
• Including resources in prompts

Resources

• API integrations
• Database connections
• File system operations

Tools

• API integration patterns
• Chunked processing
• Data processing
• Notification systems
• Semantic search implementation
• Stateful session management
• System command patterns

Sampling

• Client implementations
• Server implementations

Getting Started

To get started with the repository:

1. Clone the repository:

   git clone https://github.com/saipanyam/mcp-examples.git
   cd mcp-examples
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Explore the examples: Browse through the different directories to understand various MCP patterns and implementations.

Key Features

• Modular Design: Each example demonstrates a specific pattern or technique
• Practical Implementation: Ready-to-use code patterns for real-world applications
• Comprehensive Coverage: Covers a wide range of MCP features and capabilities
• Best Practices: Demonstrates recommended approaches for MCP implementation

Use Cases

• Building multi-agent systems
• Creating orchestrated workflows between specialized LLM services
• Implementing stateful interactions with LLMs
• Developing resource-aware applications
• Creating prompt templates for consistent LLM interactions

Contributing

Contributions to improve examples or add new ones are welcome! Please feel free to submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.