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.uvwinget 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/AIE8-MCP-Session.git
After that, you can follow from Step 2. below!
Installation
-
Clone the repository:
git clone <repository-url> cd <repository-directory> -
Configure environment variables: Copy the
.env.sampleto.envand add your Tavily API key:TAVILY_API_KEY=your_api_key_here -
🏗️ 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:
Choose an API that you enjoy using - and build an MCP server for it!
✅ Answer:
Added few new Apis in server.py.
Example:- like below
@mcp.tool()
def space_fact() -> str:
🏗️ Activity #2:
Build a simple LangGraph application that interacts with your MCP Server.
You can find details here!
✅ Answer:
This project includes a simple LangGraph client that connects to the MCP server and uses AI agents to interact with the tools.
Setup
-
Install dependencies:
uv sync -
Set up environment variables by adding your OpenAI API key to
.env:OPENAI_API_KEY=your_api_key_here -
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