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Ipfs MCP

Cloud storage integration with multi-provider support - Ipfs integration for enhanced AI capabilities

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🚀 Installation & Setup

Quick Install

# Clone the repository
git clone https://github.com/alexbakers/mcp-ipfs
cd mcp-ipfs

# Install dependencies
npm install

# Build and run
npm run build
npm start

Prerequisites

  • Node.js 18+ or Python 3.8+ (depending on implementation)
  • MCP-compatible client (Claude Desktop, Continue, etc.)
  • Basic understanding of Model Context Protocol

⚙️ Configuration

MCP Settings Configuration

{
  "mcpServers": {
    "ipfs-mcp": {
      "command": "node",
      "args": ["path/to/ipfs-mcp/dist/index.js"],
      "env": {
        "API_KEY": "your-api-key-here",
        "DEBUG": "false"
      }
    }
  }
}

Environment Variables

Set these environment variables based on your needs:

  • • API_KEY - Your API key (if required)
  • • DEBUG_MODE - Enable debug logging
  • • TIMEOUT - Request timeout in seconds

💡 Usage Examples

Basic Usage

// Example: Upload file to cloud storage
const uploadResult = await mcpClient.callTool('upload', {
  file: 'local-file.pdf',
  bucket: 'my-bucket',
  key: 'documents/file.pdf'
});

console.log('File uploaded:', uploadResult.url);

Integration Guide

This MCP server can be integrated into various AI applications and workflows. Check the GitHub repository for detailed integration examples and API documentation.

View Documentation

📊 Quick Stats

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👥 Contributors

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❓ FAQ

Which cloud providers are supported?
Support varies by server, but most work with major providers like AWS, Google Cloud, and Microsoft Azure.
How is billing handled?
The MCP server itself is free, but you pay for cloud resources used. Monitor usage to control costs.
Is auto-scaling available?
Many cloud MCP servers support auto-scaling features to automatically adjust resources based on demand.

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