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MCP Server Basic Example

MCP Server Basic Example

Public
krishnaik06/MCP-CRASH-Course

A Model Context Protocol server offering core functionality with tools for arithmetic operations and personalized greeting resources, designed for seamless integration, development, and deployment of AI-driven applications.

python
0 tools
May 30, 2025
Updated Jun 4, 2025

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MCP Server Basic Example

This is a basic example of a Model Context Protocol (MCP) server implementation that demonstrates core functionality including tools and resources.

Setup Steps

  1. Initialize the project (Go to any local folder and launch powershell or cmd):
uv init mcp-server-basic cd mcp-server-basic
  1. Create virtual environment and activate it

uv venv .venv\Scripts\activate
  1. Install dependencies:
uv add "mcp[cli]"

or

uv add -r requirements.txt

Features

The server implements the following features:

Tools

  • add(a: int, b: int): Adds two numbers
  • subtract(a: int, b: int): Subtracts second number from first

Resources

  • greeting://{name}: Returns a personalized greeting

Running the Server

To run the server with the MCP Inspector for development:

uv run mcp dev main.py

To run the server normally:

uv run mcp run

To install the server in Claude desktop app:

uv run mcp install main.py

MCP connect in VS code

  • Open folder/mcp-server-basic in vs code
  • open terminal and run below command :
uv run main.py
  • Click Cntrl+Shift+I to launch chat in vs code
  • Do login with Github and setup
  • Folow the below steps (two way to add mcp configuration for vs code user settings):

Watch the demo

Project Structure

  • main.py: Main server implementation with tools and resources
  • pyproject.toml: Project configuration and dependencies

2.0 Agentic AI And GENERATIVE AI With MCP Bootcamp

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Course Overview:

Mentors: Sourangshu Paul, Mayank Aggarwal , Krish And Sunny

Start Date:May 10th 2025

Timing: 8am to 11am IST(Saturday And Sunday)

Duration : 4-5 months

This course is designed for AI developers, machine learning engineers, data scientists, and software engineers looking to build expertise in agentic AI, multi-agent systems, and AI-powered automation. Whether you are new to AI agents or have experience in NLP and GenAI, this course will equip you with the knowledge and hands-on skills required to develop, deploy, and manage AI agents at scale. By the end of the course, you will have a strong foundation in agentic AI frameworks, multi-agent collaboration, real-world automation, and end-to-end AI deployment, along with practical experience through real-world projects.

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