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EdgeOne Pages: MCP Client and Server Implementation with Functions

@icyhd

リポジトリ

EdgeOne Pages: MCP Client and Server Implementation with Functions について

MCP Client and Server Implementation with Functions

基本情報

カテゴリ

その他

ランタイム

node

トランスポート

stdio

公開者

icyhd

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

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

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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