Tavily Web Search MCP Server
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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:
Check server.py
### 🏗️ 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:

TDQS
Scored across 3 tools
The three tools have completely distinct purposes: home repair cost estimation, dice rolling, and web searching. There is no overlap in functionality, and an agent would have no difficulty selecting the correct tool for any given task.
The naming is inconsistent with mixed conventions: 'repair_cost' and 'web_search' follow a noun_verb pattern, while 'roll_dice' uses verb_noun. This lack of a predictable naming pattern could cause confusion in automated tool selection.
With only three tools, the server appears severely under-scoped for a 'Tavily Web Search MCP Server', as web search is just one of three unrelated functions. The tools do not form a coherent set for the stated server purpose.
For a web search server, the tool surface is severely incomplete, lacking essential operations like advanced search filters, result pagination, or domain-specific searches. The inclusion of unrelated tools (repair_cost, roll_dice) further fragments the domain coverage.