Amazon MCP Server

This is a Model Context Protocol (MCP) server for scraping Amazon products and searching for products on Amazon.

Setup

1. Clone the repository:

   git clone https://github.com/r123singh/amazon-mcp-server.git

2. Navigate to the project directory:

   cd amazon-mcp-server

3. Create a virtual environment:

   python -m venv venv

4. Activate the virtual environment:

   • On Linux/macOS:

     source venv/bin/activate

   • On Windows:

     venv\Scripts\activate

5. Install dependencies:

   pip install -r requirements.txt

6. No API keys or tokens are required.

7. Configure MCP JSON: Create a mcp.json file with:

   {
     "mcpServers": {
       "amazon": {
         "command": "{PATH_TO_DIRECTORY}\\amazon-mcp-server\\venv\\Scripts\\python.exe",
         "args": [
           "{PATH_TO_DIRECTORY}\\amazon-mcp-server\\server.py"
         ]
       }
     }
   }

   Replace {PATH_TO_DIRECTORY} with the absolute path to this directory (use pwd or cd to get the path).

Available Tools

The server provides the following tools for interacting with Amazon:

• Scrape a product:
  scrape_product(product_url)
  Scrape product details (name, price, image, rating, reviews, availability, description) from a given Amazon product URL.

• Search for products:
  search_products(query, max_results)
  Search for products on Amazon by keyword and return a list of results.

Usage

Once configured, the MCP server can be started using the standard MCP client configuration. The server provides a natural language interface to interact with Amazon through the available tools.

Example usage:

• "Get details for this Amazon product: [product URL]"

• "Search Amazon for 'wireless headphones', show top 3 results"

Notes

• No API key or authentication is required.

• The server scrapes publicly available Amazon product and search pages.

• For best results, use valid Amazon product URLs and clear search queries.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.