MCP Server Basic
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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-basicCreate virtual environment and activate it
uv venv
.venv\Scripts\activateInstall dependencies:
uv add "mcp[cli]"or
uv add -r requirements.txtRelated MCP server: API MCP Server
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.pyTo run the server normally:
uv run mcp runTo install the server in Claude desktop app:
uv run mcp install main.pyMCP connect in VS code
Open folder/mcp-server-basic in vs code
open terminal and run below command :
uv run main.pyClick 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.
Tool Schema Changelog
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Maintenance
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