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mcp-on-edge1111

by HeBeiaa
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TypeScript
Remote

EdgeOne Pages: MCP Client and Server Implementation with Functions

Project Overview

This project showcases an intelligent chat application built with EdgeOne Pages Functions technology. It interacts with backend functions through a web interface, implementing a complete Model Context Protocol (MCP) workflow.

The system architecture consists of the following core components:

  • MCP Streamable HTTP Server (functions/mcp-server/index.ts)

  • MCP Client (functions/mcp-client/index.ts)

  • Backend API (functions/v1/chat/completions/index.ts) as the MCP HOST, responsible for coordinating the entire MCP workflow

Through this architecture, users can access powerful MCP tool capabilities in the browser, enabling intelligent interactions such as "generating online webpages with a single prompt."

Core Features

  • Interactive Chat Interface: Modern, responsive web interface built with Next.js and React

  • High-Performance Edge Functions: Critical business logic deployed on highly scalable EdgeOne Pages Functions

  • Complete MCP Implementation: Model Context Protocol implementation based on the latest specifications, providing powerful context management and request routing capabilities

  • OpenAI Format Compatible: Backend API fully supports OpenAI-formatted request and response handling

Streamable HTTP MCP Server

Configure remote MCP services in applications that support Streamable HTTP MCP Server.

{
  "mcpServers": {
    "edgeone-pages-mcp-server": {
      "url": "https://mcp-on-edge.edgeone.site/mcp-server"
    }
  }
}

Deploy

Deploy with EdgeOne Pages

More Templates: EdgeOne Pages

Local Development

Environment Setup

First, install dependencies and start the development server:

# Install dependencies
npm install
# Or use other package managers
# yarn install / pnpm install / bun install

# Start development server
npm run dev
# Or use other package managers
# yarn dev / pnpm dev / bun dev

Configure environment variables: Copy the .env.example file and rename it to .env, then fill in your AI service interface configuration information.

After starting, visit http://localhost:3000 in your browser to view the application.

Project Structure

  • Frontend Interface: app/page.tsx contains the main page logic and UI components

  • Backend Functions: All edge functions are in the functions directory

    • Chat API: functions/v1/chat/completions

    • MCP Server: functions/mcp-server

    • MCP Client: functions/mcp-client

Technical Documentation

Learn more about related technologies: