Federated MCP

🚀 AI Federated Network

A distributed runtime system for federated AI services with edge computing capabilities.

🚀 Quick Start

# Run the server
deno run --allow-net --allow-env --allow-read --allow-write --allow-run src/apps/deno/server.ts

✨ Features

Simplified Integration

• Eliminates the need for custom connections for each data source.
• Standardizes the connection between AI systems and enterprise tools.
• Maintains context between federated tools and datasets.

Federated Architecture

Core Components

• Federated Controller: Manages cross - server communication.
• Proxy Layer: Handles authentication between federated servers.
• Identity Management: Controls cross - instance access.

Basic Structure

System Components

• MCP Host: AI applications that require federated data access.
• MCP Server: Programs that provide federated resource access.
• MCP Client: Components that maintain federated connections.
• Federated Proxy: Manages cross - server authentication.

Real - world Use Cases

Implementation Areas

• Development tools with federated code repositories.
• Enterprise systems with distributed databases.
• Cross - organizational content repositories.
• Multi - regional business tool integrations.

Security Features

Protection Mechanisms

• Federated authentication and authorization.
• Cross - server resource isolation.
• Distributed consent management.
• Encrypted cross - server communication.
• Fine - grained permission control.

📦 Installation

The implementation is fully realized using Deno and Node.js. Let's start with the project structure:

🌐 Network Protocols

• JSON - RPC 2.0
• HTTP/REST
• WebSocket

⚡ Edge Computing

• Multi - provider support (Supabase, Cloudflare Workers, Fly.io)
• Serverless function deployment
• Real - time logging and monitoring
• Automatic scaling capabilities

🔐 Security

• Provider - specific authentication
• Secure credential storage
• Environment isolation
• Access control

📡 Runtime Features

• Task execution
• Federated support
• Intent detection
• Meeting information processing
• Webhook processing
• Real - time status monitoring
• System health check

🔧 Technical Details

Model Context Protocol (MCP) with Federated Support

The Model Context Protocol (MCP) enables interconnection between AI systems and various data sources through a standardized architecture. Here is a full implementation that adheres to the official specification:

This implementation provides a foundation for building a federated MCP system that can scale across multiple servers and maintains the security and standardization requirements of the protocol. The federated layer enables seamless communication between different MCP servers, allowing AI systems to maintain context across different tools and datasets.

This implementation supports local and remote connections through multiple transport mechanisms, including stdio for local inter - process communication and HTTP and Server - Sent Events for remote connections.

Security is maintained through strict permission negotiation and user consent requirements.

System Architecture

graph TD
    A[AI Federated Network] --> B[Core Runtime]
    B --> C[Edge Computing]
    B --> D[Network Layer]
    B --> E[Security]

    C --> F[Supabase]
    C --> G[Cloudflare]
    C --> H[Fly.io]

    D --> I[JSON - RPC]
    D --> J[HTTP/REST]
    D --> K[WebSocket]

    E --> L[Authentication]
    E --> M[Credentials]
    E --> N[Access Control]

📄 License

MIT License - See the LICENSE file for details.