EdgeOne Pages: MCP Client and Server Implementation with Functio…

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关于 EdgeOne Pages: MCP Client and Server Implementation with Functions

MCP Client and Server Implementation with Functions

EdgeOne Pages: MCP Client and Server Implementation with Functions 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"
    }
  }
}

EdgeOne Pages: MCP Client and Server Implementation with Functions 工具

Deploy HTML content to EdgeOne Pages, return the public URL

概览

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

An intelligent chat application built with EdgeOne Pages Functions technology that implements a complete Model Context Protocol (MCP) workflow, enabling browser-based access to MCP tool capabilities such as generating online webpages with a single prompt.

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

Configure remote MCP services in any app that supports Streamable HTTP MCP Server by adding the URL https://mcp-on-edge.edgeone.site/mcp-server to your MCP client configuration. For local development, clone the repository, run npm install, start with npm run dev, and set environment variables in a .env file.

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

Interactive chat interface built with Next.js and React
High-performance edge functions via EdgeOne Pages Functions
Complete MCP implementation based on the latest specifications
Backend API fully compatible with OpenAI request/response format
One-click deployment with EdgeOne Pages template

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

Generating online webpages from a single natural‑language prompt
Building intelligent chat applications with tool‑augmented LLM interactions
Prototyping and deploying MCP‑based workflows at the edge

常见问题

What dependencies are required to run this project locally?

Node.js and a package manager (npm, yarn, pnpm, or bun). An AI service API key must be provided in the `.env` file.

How is the MCP transport implemented?

The server uses the Streamable HTTP transport based on the 2025‑03‑26 version of the Model Context Protocol specification.

Where does the data live?

All critical business logic runs on EdgeOne Pages Functions (serverless edge compute); no data storage details are provided in the README.

What are the authentication requirements for the chat API?

No explicit authentication is described; environment variables for the AI service are configured locally or during deployment.

How can I deploy my own instance?

Use the “Deploy with EdgeOne Pages” button in the README or the EdgeOne Pages templates gallery.

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