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Connecting the Future: Implementing an MCP Server Compatible with Clients like Claude and VSCode

Introduction
Interoperability between AI tools and development environments is more important than ever. The Model Context Protocol (MCP) allows servers to securely interact with AI clients and dev tools. The 🎯 punkpeye/awesome-mcp-servers repository is a curated collection of production-ready and experimental MCP servers that demonstrate the wide range of what’s possible

In this article, you’ll implement a simple MCP server that connects with clients like Claude Desktop or VSCode extensions, using the examples highlighted in the Punkpeye list as inspiration. You’ll also host a public example on GitHub to replicate the setup.

What is MCP?

MCP is an open, lightweight protocol for exchanging structured messages between a server and multiple clients. Servers can offer capabilities like accessing files, performing database queries, or integrating with external APIs. Clients—such as AI models, Claude Desktop, Cursor, and others—use MCP to trigger these actions

The punkpeye/awesome-mcp-servers repo categorizes servers by functionality: browser automation, databases, developer tools, and more. This library shows that MCP servers can be used across various domains.

Demo MCP Server Overview
Example Repo: https://github.com/punkpeye/awesome-mcp-servers
It uses the Node.js WebSocket API to create a lightweight MCP server. The server is inspired by and listed in the open-source collection: punkpeye/awesome-mcp-servers.

MCP Server Capabilities

  • Accepts incoming WebSocket connections.

  • Handles MCP-style messages in the format: # command: parameters.

  • Supports basic commands:
    -ping – replies with pong
    -date – replies with current timestamp
    -echo – echoes back the message

  • Can be extended with custom logic.

Testing the Server

  1. Clone or fork the base repo: git clone https://github.com/punkpeye/awesome-mcp-servers
  2. Or create your own fork and add this Node.js server: mkdir mcp-node-server cd mcp-node-server npm init -y npm install ws
  3. Create server.js: 
const WebSocket = require('ws');
const wss = new WebSocket.Server({ port: 8080 });

wss.on('connection', ws => {
  ws.on('message', msg => {
    const text = msg.toString();
    const match = text.match(/^#(\d+)\s+(\w+):\s*(.*)$/);
    if (!match) return ws.send('error: invalid format');

    const [, tag, command, params] = match;
    let response = '';

    switch (command) {
      case 'ping':
        response = `#${tag} pong: ${params}`;
        break;
      case 'date':
        response = `#${tag} date: ${new Date().toISOString()}`;
        break;
      case 'echo':
        response = `#${tag} echo: ${params}`;
        break;
      default:
        response = `#${tag} error: unknown command "${command}"`;
    }

    ws.send(response);
  });
});

console.log('MCP server running on ws://localhost:8080');
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Testing with MCP Clients

Claude Desktop
Connect Claude to ws://localhost:8080
Send: #101 ping: Hello from Claude
Response: #101 pong: Hello from Claude

VSCode
Install the “WebSocket Client” extension.
Open new request and connect:
ws://localhost:8080
Send:
#200 echo: Hello VSCode

Conclusion

This project demonstrates the practical implementation of an MCP server capable of communicating with various MCP-compatible clients, such as Claude Desktop and VSCode. By using a lightweight Node.js + WebSocket setup, we created a functional and extensible server that adheres to the MCP message structure.

Through this work, we gained hands-on experience with:

Designing message-driven protocols,

Managing real-time bidirectional communication,

Integrating AI agents and developer tools through a unified server interface.

By referencing the public repository punkpeye/awesome-mcp-servers, this project aligns with the wider MCP ecosystem and can serve as a foundation for more advanced use cases—such as automation, AI-driven development workflows, and plugin-style architectures.

In short, this project proves that any developer can build their own MCP server to enhance and extend the capabilities of modern AI and code tools in a modular, flexible way.

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