AI Makerspace MCP Demo 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.

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:

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.

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:

Choose an API that you enjoy using - and build an MCP server for it!

🏗️ Activity #2:

Build a simple LangGraph application that interacts with your MCP Server.

You can find details here!

🤖 Activity #2: LangGraph Integration

This project includes a simple LangGraph client that connects to the MCP server and uses AI agents to interact with the tools.

Setup

1. Install dependencies:

   uv sync

2. Set up environment variables by adding your OpenAI API key to .env:

   OPENAI_API_KEY=your_api_key_here

3. Run the LangGraph client:

   uv run langgraph_client.py

How It Works

The LangGraph client:

• Connects to your MCP server via stdio transport

• Loads all available tools (e.g., web_search, roll_dice, number_fact)

• Creates a ReAct agent using openai:gpt-4o

• Demonstrates tool usage with example queries