README.md•3.3 kB
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## <h1 align="center" id="heading">AI Makerspace: MCP Session Repo for Session 13</h1>
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:
```bash
git clone https://AI-Maker-Space/AIE7-MCP-Session.git
```
After that, you can follow from Step 2. below!
## Installation
1. **Clone the repository**:
```bash
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:
Choose an API that you enjoy using - and build an MCP server for it!
##### ✅ Answer:
Built a client that connects with [ExchangeRate-API](https://www.exchangerate-api.com/docs/overview)
See code in this repository and screen capture of it working!
<img src="Screenshot 2025-08-06 at 9.29.51 AM.png" width="599" alt="screen capture of MCP use in Cursor" />
### 🏗️ Activity #2:
Build a simple LangGraph application that interacts with your MCP Server.
You can find details [here](https://github.com/langchain-ai/langchain-mcp-adapters)!
##### ✅ Answer:
See my [GitHub Homework Branch](https://github.com/julie-berlin/aie-cohort-7/tree/feat/homework/13_MCP/ACTIVITY_2.md) for implementation!
