MCP Math Tools Server

This project demonstrates a Python implementation of the Model Context Protocol (MCP). It builds a functional MCP Server that exposes mathematical capabilities (Tools) and static data (Resources) to MCP Clients, such as AI assistants or the MCP Inspector.

Project Overview

The goal of this project is to create a standardized server that bridges the gap between an AI model and local computational logic.

Key Features:

• Tools: Exposes executable functions (add_numbers, multiply_numbers) that an AI can call to perform calculations.
• Resources: Exposes structured data (math://constants) that an AI can read as context.
• Architecture: Built using the official mcp Python SDK and tested using the generic MCP Inspector.

Prerequisites

• Python 3.10+
• Node.js & NPM (Required only to run the MCP Inspector for testing)

Setup & Installation

1. Clone the Repository

   git clone https://github.com/shivamgravity/mcp
   cd mcp

2. Set Up Virtual Environment It is recommended to use a virtual environment to manage dependencies.

   # Create venv
   python -m venv venv
   
   # Activate venv (Windows)
   venv\Scripts\activate
   
   # Activate venv (Mac/Linux)
   source venv/bin/activate
   
   # To deactivate the virtual environment, run this command
   deactivate

3. Install Dependencies Install the official Python SDK for MCP.

   pip install mcp

How to Run (Testing with Inspector)

Since this is a protocol server, it needs a client to interact with. We use the official MCP Inspector web interface for testing.

1. Start the Server via Inspector: Run the following command in your terminal (ensure your venv is active):

   npx @modelcontextprotocol/inspector python server.py

2. Access the UI: Open your browser and navigate to the URL provided in the terminal (usually http://localhost:6274 or http://localhost:5173).

3. Connect & Test:

   • Click Connect.
   • Tools Tab: Select add_numbers, enter arguments (e.g., a=10, b=5), and click "Run Tool" to see the result.
   • Resources Tab: Click on the math://constants resource to read the structured JSON data.

Solution Architecture

This project follows the Model Context Protocol standard:

• Server (server.py):

  • Uses the FastMCP class to initialize the server instance.
  • Tools: Functions decorated with @mcp.tool() are exposed as executable logic. The server handles the JSON-RPC communication to receive arguments and return results.
  • Resources: Functions decorated with @mcp.resource() act as data endpoints. They return structured JSON data (MIME type application/json) representing math constants.

• Communication:

  • The server communicates over stdio (Standard Input/Output), allowing it to be easily piped into any MCP-compliant client (like Claude Desktop or the MCP Inspector).