A demo implementation of a MCP server (consuming a dummy API) and basic client.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"demo-mcp-server-client-implementation": {
"command": "<see-readme>",
"args": []
}
}
}Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
I wrote an article and created a video about MCPs and why they're useful in my opinion.
No automated test available for this server. Check the GitHub README for setup instructions.
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
No package registry to scan.
Be the first to review
Have you used this server?
Share your experience — it helps other developers decide.
Sign in to write a review.
Others in developer-tools
Manage Supabase projects — databases, auth, storage, and edge functions
XcodeBuildMCP provides tools for Xcode project management, simulator management, and app utilities.
A Model Context Protocol (MCP) server and CLI that provides tools for agent use when working on iOS and macOS projects.
Asynchronous coordination layer for AI coding agents: identities, inboxes, searchable threads, and advisory file leases over FastMCP + Git + SQLite
MCP Security Weekly
Get CVE alerts and security updates for Demo Mcp Server Client Implementation and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.
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!
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.
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 :-)