Basic example of a Model Context Protocol (MCP) server implementation that demonstrates core functionality including tools and resources.
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
- Initialize the project (Go to any local folder and launch powershell or cmd):
uv init mcp-server-basic
cd mcp-server-basic
Create virtual environment and activate it
uv venv
.venv\Scripts\activate
- 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 numberssubtract(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):
Project Structure
main.py: Main server implementation with tools and resourcespyproject.toml: Project configuration and dependencies
2.0 Agentic AI And GENERATIVE AI With MCP Bootcamp
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.
Tools (2)
addAdds two numberssubtractSubtracts second number from firstConfiguration
{
"mcpServers": {
"mcp-basic": {
"command": "uv",
"args": ["run", "main.py"]
}
}