Multi-Context Processing (MCP) Project

This project is based on the Build Rich Context AI Apps with Anthropic course from DeepLearning.AI. The course teaches how to build sophisticated AI applications using Anthropic's Claude model and the Multi-Context Processing (MCP) framework.

Course Credits

Special thanks to:

• DeepLearning.AI for providing the comprehensive course
• Anthropic for their Claude model and MCP framework
• The course instructors for their excellent teaching materials

Project Structure

The project consists of several key components:

Core Notebooks

• mcp_prompt_resource.ipynb - Demonstrates prompt engineering and resource management
• classic_tool_calling.ipynb - Shows traditional tool calling approaches
• mcp_multi_server.ipynb - Implements multi-server architecture
• mcp_client_fastMCP.ipynb - Client implementation using FastMCP
• mcp_server_fastMCP.ipynb - Server implementation using FastMCP
• deploy.ipynb - Deployment and production considerations

Project Directories

• mcp_project/ - Main project directory containing core implementations
• mcp/ - MCP framework related files
• images/ - Project-related images and assets

Dependencies

The project requires the following Python packages:

anthropic>=0.51.0
arxiv>=2.2.0
mcp>=1.7.1
pypdf2>=3.0.1
python-dotenv>=1.1.0
uv

Key Features

1. Multi-Context Processing

   • Advanced context management
   • Parallel processing capabilities
   • Efficient resource utilization

2. FastMCP Implementation

   • High-performance client-server architecture
   • Optimized for production environments
   • Scalable design patterns

3. Tool Integration

   • Classic tool calling patterns
   • Modern API integrations
   • Resource management

Getting Started

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt

3. Set up your environment variables (create a .env file with your Anthropic API key)
4. Run the notebooks in sequence to understand the implementation

Best Practices

• Always use environment variables for sensitive information
• Follow the notebook sequence for proper understanding
• Test thoroughly before deployment
• Monitor resource usage in production

License

This project is for educational purposes and follows the licensing terms of the original course materials.

Contributing

Feel free to submit issues and enhancement requests!