mcp-server

AI Makerspace: MCP Session Repo for Session 13

This project is a demonstration of the MCP (Model Context Protocol) server, which utilizes the Tavily API for web search capabilities. The server is designed to run in a standard input/output (stdio) transport mode.

Related MCP server: metasearch-mcp

Project Overview

The MCP server is set up to handle web search queries using the Tavily API. It is built with the following key components:

• TavilyClient: A client for interacting with the Tavily API to perform web searches.

Prerequisites

• Python 3.13 or higher

• A valid Tavily API key

⚠️NOTE FOR WINDOWS:⚠️

You'll need to install this on the Windows side of your OS.

This will require getting two CLI tool for Powershell, which you can do as follows:

• winget install astral-sh.uv

• winget install --id Git.Git -e --source winget

After you have those CLI tools, please open Cursor into Windows.

Then, you can clone the repository using the following command in your Cursor terminal:

git clone https://AI-Maker-Space/AIE7-MCP-Session.git

After that, you can follow from Step 2. below!

Installation

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Configure environment variables: Copy the .env.sample to .env and add your Tavily API key:

   TAVILY_API_KEY=your_api_key_here

3. 🏗️ Add a new tool to your MCP Server 🏗️

Create a new tool in the server.py file, that's it!

Running the MCP Server

To start the MCP server, you will need to add the following to your MCP Profile in Cursor:

NOTE: To get to your MCP config. you can use the Command Pallete (CMD/CTRL+SHIFT+P) and select "View: Open MCP Settings" and replace the contents with the JSON blob below.

{
    "mcpServers":  {
        "mcp-server": {
            "command" : "uv",
            "args" : ["--directory", "/PATH/TO/REPOSITORY", "run", "server.py"]
        }
    }
}

The server will start and listen for commands via standard input/output.

Usage

The server provides a web_search tool that can be used to search the web for information about a given query. This is achieved by calling the web_search function with the desired query string.

Activities:

There are a few activities for this assignment!

🏗️ Activity #1: ✅

• Built an MCP tool for the Chess.com API to fetch player details and ratings.

• Implemented as chess_player_stats in server.py and routed via chess_router.py.

🏗️ Activity #2: LangGraph + MCP Chess App ✅

• Implemented a LangGraph ReAct agent that connects to the local MCP server and automatically selects chess tools for natural-language queries.

• Requirements:

  • Install Stockfish and ensure stockfish is in your PATH (or set STOCKFISH_PATH in .env).

  • Set .env variables: OPENAI_API_KEY, CHESS_USERNAME. Optional: TAVILY_API_KEY, STOCKFISH_PATH.

• Run:

  uv run main.py