Building a Remote MCP Server and Connecting It to Any MCP Client

1. Introduction

The Model Context Protocol (MCP) is a new standard that allows AI models—such as Claude, ChatGPT, or Gemini—to connect with external tools, APIs, and systems in a secure and controlled way. Instead of being limited to conversation, the AI can interact with real-world environments and perform useful actions.

In this article, I explain how to build a simple MCP Server, how it works internally, how to connect it to any MCP Client, and why this technology is becoming essential for developers and small teams who want to integrate AI into their workflows.

1. What Is an MCP Server?

An MCP Server is a small application that exposes tools, functions, or resources to an AI model through the MCP protocol.
It works like a bridge:

AI Model → MCP Client → Your MCP Server → Tools / APIs / Files / Databases

An MCP Server can provide:

File access (read/write)

API calls

Automation scripts

Database query tools

Cloud integrations

Custom business logic

Any MCP-compatible client can communicate with it, including:

Claude Desktop

MCP plugins for VSCode

Custom MCP Clients

1. How MCP Architecture Works

The general architecture looks like this:

AI Model → MCP Client → WebSocket/STDIO → MCP Server → Your Tools

The key idea:

The client handles the communication

The server exposes the tools

The AI model decides when to use them

This separation keeps everything secure and modular.

1. Why Build Your Own MCP Server?

Building an MCP Server lets your AI assistant interact directly with your environment.

Benefits:

Automate repetitive tasks

Trigger scripts or system actions

Query databases

Access real-time information

Build personalized developer tools

Create AI-powered copilots for your workspace

It transforms the AI into a hands-on assistant, not just a chatbot.

1. Building a Simple MCP Server (Step-by-Step)

Here is the simplest structure for an MCP Server using Node.js.

Step 1 — Create a new project
npm init -y
npm install @modelcontextprotocol/sdk typescript ts-node
npx tsc --init

Step 2 — Create the server file

src/server.ts

import {
Server,
Tool,
StdioServerTransport,
} from "@modelcontextprotocol/sdk";

const server = new Server(
{
name: "example-mcp-server",
version: "1.0.0",
},
{
capabilities: { tools: {} },
}
);

server.tool("getDate", new Tool(async () => {
return { now: new Date().toISOString() };
}));

server.connect(new StdioServerTransport());

Step 3 — Build and run
npm run build
node dist/server.js

1. Connecting the Server to Claude Desktop

Edit the Claude config file:

Windows:
%APPDATA%/Claude/claude_desktop_config.json

Mac:
~/Library/Application Support/Claude/claude_desktop_config.json

Add:

{
"mcpServers": {
"myServer": {
"command": "node",
"args": ["path/to/dist/server.js"]
}
}
}

Restart Claude — the tool will appear automatically.

1. Optional: Deploying to the Cloud

You can deploy your MCP Server to:

Google Cloud Run

Cloudflare Workers

Vercel Edge Functions

Any container-based system

This allows AI tools to access your server remotely and securely.

1. Example Use Cases ✔ Developer automation

Let Claude run scripts, lint code, or review PRs using local tools.

✔ Internal business tools

Let AI access internal APIs, inventory systems, or customer data (safely).

✔ File automation

Automatically generate documents, update logs, and manage repositories.

✔ Cloud operations

Interact with cloud services via API integrations.

1. Good Practices

Do not expose tools without authentication

Log all interactions for auditing

Validate all input schemas

Apply rate limiting

Keep the server’s environment isolated

These prevent unsafe use and keep the MCP server secure.

1. Conclusion

Building an MCP Server is a powerful way to connect AI models with real-world systems. With only a few lines of code, you can expose tools, automate workflows, and create intelligent assistants that work directly with your environment. Whether you deploy it locally or in the cloud, MCP opens the door to the next generation of AI-powered automation.

1. References

Cloudflare Agents Docs (2024). Build a Remote MCP Server.

Google Cloud Blog (2024). Deploy a Remote MCP Server to Cloud Run.

Composio (2024). MCP Server: Step-by-Step Guide to Building from Scratch.

Anthropic (2024). Model Context Protocol Specification.

Comments

[AHMED HASAN AKHTAR OVIEDO]

Tu artículo está bien armado y aterriza el concepto de MCP sin marear al lector. Explicas el flujo técnico con claridad y el paso a paso se entiende al toque. Quizá podrías compactar un poco la parte final para que el cierre sea más contundente, pero en general quedó profesional y directo.