MCP Server/Client: QuickStart & Battle-Tested Integration

Explore a hands-on MCP demo: server with dummy API consumption and basic client. Quick-start, test workflows—your integration blueprint, battle-tested!

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About MCP Server/Client

What is MCP Server/Client: QuickStart & Battle-Tested Integration?

MCP stands for Model Context Protocol, a standardized way to integrate backend services with client applications. This implementation demonstrates a trio of components: a backend service, an MCP server, and an MCP client (like an AI chatbot). The server acts as a bridge, exposing the backend’s capabilities through MCP-compliant interfaces. Think of it as middleware that simplifies complex system interactions — a concept I’ve explored in depth in my article and video series.

How to use MCP Server/Client: QuickStart & Battle-Tested Integration?

Let’s walk through the setup:

Create a .env file in the client folder with your OpenAI API key.
Run npm install in each project folder (service, server, client).
Execute npm run dev in all three folders simultaneously using separate terminal tabs.

Once running, the chatbot client will interact with the MCP server, which in turn communicates with the backend service — all powered by Node.js 23+’s native TypeScript support.

MCP Server/Client Features

Key Features of MCP Server/Client: QuickStart & Battle-Tested Integration?

TypeScript-first execution: No compilation steps thanks to modern Node.js capabilities.
Modular architecture: Clear separation between service logic, MCP server, and client applications.
AI integration showcase: Demonstrates practical use of MCP to extend chatbot functionality with custom backend data.

One of the standout aspects is its battle-tested nature — the code has been stress-tested in my own projects, though minor bugs may still lurk in edge cases.

Use Cases of MCP Server/Client: QuickStart & Battle-Tested Integration?

Imagine building an e-commerce platform where:

The service handles product data storage
The MCP server exposes inventory APIs
The client could be a voice assistant recommending products

MCP Server/Client FAQ

FAQ from MCP Server/Client: QuickStart & Battle-Tested Integration?

Why Node.js 23+?

It’s the first LTS version with built-in TypeScript support, eliminating the need for tsc compilation steps. Older versions will throw runtime errors.

Can I replace the OpenAI client?

Absolutely! The MCP server acts as an abstraction layer — you could swap the chatbot with any other client that consumes MCP-compliant endpoints.

How battle-tested is this really?

While the core concepts are proven, this is a minimal viable demo. Production use would require error handling, authentication, and rate limiting enhancements.

Content

Example MCP Server + Client Implementation

I wrote an article and created a video about MCPs and why they're useful in my opinion.

This demo project contains a backend service / API that's consumed by a MCP server which exposes it in a standardized way to MCP clients - like the example MCP client (a very simple AI chatbot) that's also part of this project.

Important: All three parts (service, server, client) rely on Node.js being able to execute TypeScript without a compilation step. Yes, modern Node.js can do that! Make sure you have Node.js 23.x or higher installed!

Configuration

Add a .env file inside the client folder (next to the package.json file there) and add the following content to it:

OPENAI_API_KEY=<your-open-ai-key>

In each folder (service, server, client) run npm install to install required dependencies.

Running Service, MCP Server & MCP Client

For each part, navigate into the respective folder (service, server and client) and run npm run dev (with Node.js 23+). Keep each process running.

The "client" process is a simply AI chatbot using OpenAI behind the scenes. This chatbot has the custom MCP server "installed" - therefore, this AI chat application is able to store and retrieve custom information via the backend service created in the "service" folder.

Bugs are possible - it's just a basic demo implementation :-)

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