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EdgeOne Pages: MCP Client and Server Implementation with Functions

@xixian

代码仓库

关于 EdgeOne Pages: MCP Client and Server Implementation with Functions

MCP Client and Server Implementation with Functions

#mcp#edgeone#chat-application

基本信息

分类

其他

运行时

node

传输方式

stdio

发布者

xixian

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is EdgeOne Pages: MCP Client and Server Implementation with Functions?

This project is an intelligent chat application built with EdgeOne Pages Functions technology that implements a complete Model Context Protocol (MCP) workflow. It is for developers who want to deploy MCP servers and clients on EdgeOne Pages and enable browser-based tool interactions.

How to use EdgeOne Pages: MCP Client and Server Implementation with Functions?

To use the remote MCP server, configure it in any application that supports Streamable HTTP MCP Server by adding the JSON snippet provided. For local development, install dependencies with npm install, copy .env.example to .env and fill in AI service credentials, then run npm run dev and visit http://localhost:3000.

Key features of EdgeOne Pages: MCP Client and Server Implementation with Functions

  • Interactive chat interface built with Next.js and React
  • Critical business logic deployed on scalable EdgeOne Pages Functions
  • Complete MCP implementation based on the latest Streamable HTTP specification
  • Backend API fully compatible with OpenAI request and response formats

Use cases of EdgeOne Pages: MCP Client and Server Implementation with Functions

  • Generate online webpages with a single prompt via intelligent tool calls
  • Build browser-based chat applications that orchestrate MCP tools
  • Deploy high‑performance edge functions for context‑aware AI interactions

FAQ from EdgeOne Pages: MCP Client and Server Implementation with Functions

What MCP specification does this implement?

It implements the Model Context Protocol based on the 2025‑03‑26 version of Streamable HTTP transport.

What are the runtime dependencies?

Node.js and a package manager (npm, yarn, pnpm, or bun) are required for local development. Deployment is through EdgeOne Pages.

Which AI service can I use?

The project supports any AI service that provides an OpenAI‑compatible API; you configure the endpoint and key in the .env file.

How is the architecture structured?

The system has three core edge functions: an MCP Streamable HTTP Server, an MCP Client, and a backend API that acts as the MCP HOST to coordinate the workflow.

Is authentication or authorization required?

The README does not mention authentication; it relies on the user’s AI service credentials configured via environment variables.

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