MCP Server Basic Example

# 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): ```bash uv init mcp-server-basic cd mcp-server-basic ``` 2. # Create virtual environment and activate it ```bash uv venv .venv\Scripts\activate ``` 3. Install dependencies: ```bash uv add "mcp[cli]" ``` or ```bash 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: ```bash uv run mcp dev main.py ``` To run the server normally: ```bash uv run mcp run ``` To install the server in Claude desktop app: ```bash 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 : ```bash 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](videos/mcp%20basic.mp4) ## 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  **[Enroll Now](https://www.krishnaik.in/liveclass/2.0%20Live%20Agentic%20AI%20And%20Generative%20AI%20Application%20With%20Cloud%20Bootcamp)** 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.