ipfs-kit-py 0.3.0

Python toolkit for IPFS with high-level API, cluster management, tiered storage, and AI/ML integration

IPFS Kit Python - Production Ready MCP Server

IPFS Kit is a comprehensive, production-ready Python toolkit for working with IPFS (InterPlanetary File System) technologies. It provides a unified Model Context Protocol (MCP) server for IPFS operations, cluster management, tiered storage, and AI/ML integration.

🎉 Now Production Ready! This project has been transformed from 700+ cluttered files to a clean, organized structure with comprehensive documentation. See FINAL_PRODUCTION_STATUS.md for complete details.

🚀 Quick Start

Production MCP Server

Start the production MCP server in seconds:

# Direct execution
python3 final_mcp_server_enhanced.py --host 0.0.0.0 --port 9998

# Docker deployment
docker-compose up -d

# Development mode with debug logging
python3 final_mcp_server_enhanced.py --debug

Installation

# Development installation
git clone https://github.com/your-repo/ipfs_kit_py.git
cd ipfs_kit_py
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

# Package installation (when published)
pip install ipfs_kit_py[full]

🌟 Key Features

Production MCP Server

• FastAPI-based REST API with comprehensive IPFS operations
• JSON-RPC 2.0 protocol support for Model Context Protocol compatibility
• Mock IPFS implementation for reliable testing and development
• Health monitoring with /health endpoint and metrics
• Comprehensive logging with structured output and debug modes
• Docker deployment with optimized containers and compose configuration

IPFS Operations

• Content Management: Add, retrieve, pin, and list IPFS content
• Mock Storage: Realistic IPFS behavior for testing without requiring IPFS daemon
• CID Generation: Proper content addressing with SHA-256 based CIDs
• Error Handling: Robust error handling with detailed error messages

Developer Experience

• Command-line Interface: Easy-to-use CLI with configurable options
• Auto-generated Documentation: Interactive API docs at /docs endpoint
• Validation Tools: Built-in server validation and testing utilities
• Hot Reload: Development mode with automatic code reloading

📋 API Endpoints

The MCP server provides the following endpoints:

Health & Status

• GET /health - Health check and server status
• GET /metrics - Server metrics and statistics
• GET /docs - Interactive API documentation

JSON-RPC Interface

• POST /jsonrpc - MCP protocol endpoint for tool execution

IPFS Operations (via JSON-RPC)

• ipfs_add - Add content to IPFS storage
• ipfs_cat - Retrieve content by CID
• ipfs_pin_add - Pin content for persistence
• ipfs_pin_ls - List pinned content
• ipfs_refs - List references and links

🐳 Docker Deployment

Using Docker Compose (Recommended)

# Start the service
docker-compose up -d

# View logs
docker-compose logs -f

# Stop the service
docker-compose down

Manual Docker Build

# Build the image
docker build -t ipfs-kit-mcp .

# Run the container
docker run -p 9998:9998 ipfs-kit-mcp

🔧 Configuration

Environment Variables

• IPFS_KIT_HOST - Server host (default: 127.0.0.1)
• IPFS_KIT_PORT - Server port (default: 9998)
• IPFS_KIT_DEBUG - Enable debug mode (default: false)
• PYTHONUNBUFFERED - Ensure unbuffered output for Docker

Command Line Options

python3 final_mcp_server_enhanced.py --help

Options:

• --host HOST - Host to bind to (default: 127.0.0.1)
• --port PORT - Port to bind to (default: 9998)
• --debug - Enable debug mode with detailed logging

Examples

For practical examples of using IPFS Kit, see the examples directory, which includes:

• Basic usage examples
• FSSpec integration examples
• Cluster management examples
• Performance profiling examples
• Filesystem journal examples
• Journal monitoring and visualization examples
• AI/ML integration examples
• AI/ML visualization examples
• Data science workflow examples
• High-level API usage examples
• Tiered cache performance examples
• Probabilistic data structures examples
• Practical integration examples

Contributing to Documentation

We welcome contributions to improve the documentation! If you find errors, have suggestions, or want to add examples, please submit a pull request or open an issue.

When contributing to documentation:

1. Follow the existing style and formatting
2. Provide practical examples where appropriate
3. Explain concepts clearly with diagrams when appropriate
4. Link related documentation
5. Test examples before submitting

Getting Help

If you need help with IPFS Kit:

• Check the README.md for quick start guides
• Search the documentation for specific topics
• Look at the examples in the examples directory
• Open an issue on GitHub if you find a bug or have a feature request