MCP Sample Project

This is a simple Node.js example demonstrating the Model Context Protocol (MCP).

First project is related to Python

python is more suited to be deployed on Render than Vercel

python has requirements.txt , something similar to package.json

Vercel is not optimized for Python/Flask by default

Vercel is designed primarily for Node.js, Next.js, React, etc.

However, you can deploy Flask using:

Serverless functions via Python (using Vercel's experimental Python support)

Or better: deploy Flask to Render.com, Fly.io, or Railway.app

What is MCP?

MCP (Model Context Protocol) is a standard to connect AI models with external data/tools via a protocol.

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Another parallel Project Structure

• server.js - MCP server exposing API actions
• client.js - MCP client calling the server
• package.json - dependencies & scripts

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Setup Instructions

1. Clone this repo:

   git clone https://github.com/sanjeetkumaritoutlook-user/mcp-sample-project.git
   cd mcp-sample-project

Start MCP server

npm run start-server

Start MCP client

npm run start-client

You should see the client fetch data from the server.

Deployment

You can deploy the MCP server to any cloud platform supporting Node.js (Heroku, Vercel, AWS).

Update the client URL (http://localhost:4000) to your deployed server URL.

simple Node.js MCP server + client sample project with:

MCP server exposing one sample action (getData)

MCP client calling the server

README.md with setup & deployment instructions

GitHub-friendly structure

Understand MCP Basics

MCP is a standard protocol to let AI models connect with external data/tools.

Your project will include an MCP server (which exposes data/tools) and an MCP client (which interacts with the server).

Set Up Your Development Environment

Choose your preferred language: MCP spec provides SDKs in popular languages like Python, JavaScript/TypeScript, etc.

Install necessary dependencies (e.g., MCP SDK, web framework).

Implement MCP Server

The MCP server exposes APIs your AI model can query.

Example: if your AI needs access to a database, MCP server will handle queries to that database.

Use MCP SDK or follow the MCP spec to implement the server interface.

Implement MCP Client

The client is embedded in your AI app or chatbot.

It sends requests to the MCP server to retrieve data or execute actions.

Again, use SDK or REST calls according to the MCP spec.

Test Locally

Run the MCP server locally.

Run your client and test communication.

Make sure your AI can query data correctly through MCP.

(Optional) Set Up GitHub Actions

Add GitHub Actions workflow (.github/workflows/deploy.yml) to automate tests, builds, or deployment.

Example: run tests on push to main.

Prepare for Deployment

Add configuration files: package.json (Node.js) or requirements.txt (Python).

Create a README.md explaining your project and how to run it.

Add .gitignore to exclude node_modules, virtual environments, etc.