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MCPhoenix

MCP.Pizza Chef: jmanhype

MCPheonix is a robust MCP server built on Elixir's Phoenix Framework, offering real-time Server-Sent Events, JSON-RPC endpoints, and a self-healing distributed architecture. It supports event publish/subscribe, tool invocation, and edge computing via Cloudflare Workers, enabling seamless AI model interaction with application data and functionality through a unified, extensible interface.

Use This MCP server To

Stream real-time AI model notifications via Server-Sent Events Enable AI clients to invoke tools and access resources via JSON-RPC Implement event-driven AI workflows with publish/subscribe mechanisms Deploy self-healing distributed MCP servers on Cloudflare Durable Objects Run AI context processing at the edge using Cloudflare Workers Integrate image generation and task management tools in AI workflows Build extensible MCP server architectures for custom AI applications

README

MCPheonix

A simplified implementation of the Model Context Protocol (MCP) server using Elixir's Phoenix Framework.

Overview

MCPheonix is an intelligent, self-healing, distributed AI event system using Model Context Protocol and Elixir's Phoenix Framework. It provides a server that implements the Model Context Protocol, allowing AI models to interact with your application data and functionality through a unified interface.

Features

  • Server-Sent Events (SSE) stream for real-time notifications
  • JSON-RPC endpoint for client requests
  • Simple resource system
  • Event publish/subscribe mechanism
  • Basic tool invocation
  • Flux image generation integration
  • Dart task management integration
  • Extensible MCP server architecture
  • Self-healing distributed architecture via Cloudflare Durable Objects
  • Edge computing capabilities through Cloudflare Workers

Self-Healing Distributed Architecture

MCPheonix implements a sophisticated self-healing system through its integration with Cloudflare Durable Objects and Workers, creating a distributed, resilient architecture that can withstand failures and ensure continuous operation.

Key Components

  • Durable Objects: Stateful serverless components running on Cloudflare's global edge network that maintain consistency even across failures.
  • Edge Distribution: Critical application state is replicated across Cloudflare's global network, ensuring availability even during regional outages.
  • Automatic Recovery: If an instance becomes unavailable, the system automatically recreates it with consistent state from durable storage.
  • Real-time Communication: WebSocket support enables instant recovery coordination and state synchronization across the distributed system.
  • Event-Driven Architecture: Components react to state changes through a publish/subscribe model, allowing the system to self-heal and adapt to failures.

For detailed information on the implementation, see the Cloudflare Integration documentation.

Getting Started

Prerequisites

  • Elixir 1.14 or higher
  • Erlang 25 or higher
  • Phoenix 1.7.0 or higher
  • Python 3.9+ (for Flux and Dart integration)
  • Node.js 18+ (for Dart MCP server)
  • Cloudflare account (for Durable Objects integration)

Installation

  1. Clone the repository
git clone https://github.com/yourusername/mcpheonix.git
cd mcpheonix
  1. Install dependencies
mix deps.get
  1. Configure the Cloudflare integration

    • Create a Cloudflare Worker using the template in cloudflare/durable-objects-worker.js
    • Deploy it to your Cloudflare account
    • Set the environment variables:
      • CLOUDFLARE_WORKER_URL: URL of your deployed worker
      • CLOUDFLARE_ACCOUNT_ID: Your Cloudflare account ID
      • CLOUDFLARE_API_TOKEN: API token with Workers and DO permissions
  2. Configure the Flux integration (if using image generation)

  3. Configure the Dart integration (if using task management)

  4. Start the server

mix phx.server

The server will be available at http://localhost:4001.

Adding Custom MCP Servers

MCPheonix is designed to work with multiple MCP servers. This system includes a flexible infrastructure for integrating custom MCP servers through:

  1. Simple JSON Configuration: Define your server settings in priv/config/mcp_servers.json:
{
  "mcpServers": {
    "your_server_id": {
      "command": "/path/to/executable",
      "args": ["arg1", "arg2"],
      "env": {
        "ENV_VAR1": "value1",
        "ENV_VAR2": "value2"
      },
      "tools": {
        "your_tool": {
          "description": "Description of your tool",
          "parameters": [
            { "name": "param1", "type": "string", "description": "Parameter description" }
          ]
        }
      }
    }
  }
}
  1. Automatic Server Management: Servers are automatically loaded and managed during application startup.

For comprehensive implementation details, including the Elixir architecture, server lifecycle management, and protocol handling, see the Adding MCP Servers documentation.

MCP Endpoints

  • SSE Stream: GET /mcp/stream

    • Establishes a Server-Sent Events stream for receiving real-time notifications
    • Returns a client ID in the response headers
  • JSON-RPC: POST /mcp/rpc

    • Accepts JSON-RPC 2.0 requests
    • Client ID can be provided in the x-mcp-client-id header or will be generated if missing

Built-in Capabilities

Resources

MCPhoenix FAQ

How does MCPheonix handle real-time communication?
MCPheonix uses Server-Sent Events (SSE) to stream real-time notifications to connected clients.
What makes MCPheonix's architecture self-healing?
It leverages Cloudflare Durable Objects to maintain distributed state and recover automatically from failures.
Can MCPheonix integrate with external tools?
Yes, it supports basic tool invocation and integrations like Flux image generation and Dart task management.
How does MCPheonix support edge computing?
It runs on Cloudflare Workers, enabling low-latency AI context processing at the network edge.
What protocol does MCPheonix implement for client-server communication?
MCPheonix implements the Model Context Protocol with JSON-RPC endpoints for client requests.
Is MCPheonix extensible for custom AI workflows?
Yes, its architecture is designed to be extensible for adding new resources, tools, and event handlers.
What programming language and framework is MCPheonix built with?
MCPheonix is built using Elixir and the Phoenix Framework.
How does MCPheonix manage event-driven AI interactions?
It provides an event publish/subscribe mechanism to coordinate AI model events and responses.