Beatport Metadata Extractor

Overview

This AWS Lambda function extracts metadata from Beatport track URLs, including preview audio URLs, cover artwork, and track information. It's designed to be deployed as a serverless function using the Serverless Framework with Docker containerization.

Related MCP server: spotify-mcp

Features

• Track Metadata Extraction: Retrieves comprehensive track information from Beatport URLs

• Audio Preview URLs: Extracts lofi/preview audio file URLs

• Cover Artwork: Fetches high-quality cover image URLs (500x500)

• Serverless Architecture: Runs on AWS Lambda with optimized performance

• Docker-based Deployment: Uses container images for consistent execution environment

Example Usage

Input:

https://www.beatport.com/track/junin-shane-robinson-remix/7226500

Output:

{
    'data': {
        'audio_url': 'https://geo-samples.beatport.com/track/09f9bd4d-10cd-4dbe-a084-8e91564c40d1.LOFI.mp3',
        'image_url': 'https://geo-media.beatport.com/image_size/500x500/d6e0659f-7da9-4ebc-99dd-8f7e05a16214.jpg',
        'platform': 'beatport',
        'title': 'Jelly For The Babies - Junin (Shane Robinson Remix) [PHW Elements] | Music & Downloads on Beatport',
        'url': 'https://www.beatport.com/track/junin-shane-robinson-remix/7226500'
    }
}

Prerequisites

• Node.js (v14 or higher) and npm

• Python 3.11

• Docker

• Serverless Framework

• AWS Account with appropriate credentials configured

• AWS CLI configured with your credentials

Getting Started

1. Clone the Repository

git clone <your-repo-url>
cd <repo-name>

2. Install Dependencies

Install Node.js dependencies (Serverless plugins):

npm install

Install Python dependencies:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Configure Environment Variables

Create a .env file in the project root:

# Optional: Override default AWS region
AWS_REGION=us-east-1

4. Local Testing

Test the function locally without deploying:

make local

Or run the handler directly:

make test

Deployment

Deploy to AWS

Deploy to the development stage:

serverless deploy --stage dev

Deploy to production:

serverless deploy --stage prod

The deployment process will:

1. Build a Docker image from the Dockerfile

2. Push the image to AWS ECR

3. Create/update the Lambda function with the container image

4. Configure the function with 1024MB memory and 120s timeout

Invocation

After successful deployment, you can invoke the deployed function by using the following command:

serverless invoke --stage dev --function beatport --path tests/events/beatport.json

Or using the Makefile shortcut:

make remote

Which should result in a response similar to:

{
    "data": {
        "audio_url": "https://geo-samples.beatport.com/track/09f9bd4d-10cd-4dbe-a084-8e91564c40d1.LOFI.mp3",
        "image_url": "https://geo-media.beatport.com/image_size/500x500/d6e0659f-7da9-4ebc-99dd-8f7e05a16214.jpg",
        "platform": "beatport",
        "title": "Jelly For The Babies - Junin (Shane Robinson Remix) [PHW Elements] | Music & Downloads on Beatport",
        "url": "https://www.beatport.com/track/junin-shane-robinson-remix/7226500"
    }
}

Local Development

You can invoke your function locally by using the following command:

serverless invoke local --stage dev --function beatport --path tests/events/beatport.json

Or using the Makefile:

make local

API Endpoints

The Lambda function is exposed via HTTP API Gateway with both GET and POST support:

POST Request

curl -X POST https://your-api-url/beatport \
  -H "Content-Type: application/json" \
  -d '{"url": "https://www.beatport.com/track/junin-shane-robinson-remix/7226500"}'

GET Request

curl "https://your-api-url/beatport?url=https://www.beatport.com/track/junin-shane-robinson-remix/7226500"

Testing API Locally

# Set up environment variables in .env
API_URL=https://your-api-url/beatport
TEST_URL=https://www.beatport.com/track/junin-shane-robinson-remix/7226500

# Run the API test script
python tests/invoke_api.py

MCP Server (Claude Desktop Integration)

The Beatport scraper can be used as an MCP (Model Context Protocol) server, allowing Claude Desktop to extract metadata from Beatport tracks directly in conversations.

What is MCP?

MCP (Model Context Protocol) allows Claude Desktop to interact with external tools and services. This integration exposes the Beatport scraper as a tool that Claude can automatically use when you ask about Beatport tracks.

Quick Start

1. Install MCP dependencies:

   pip install -r mcp_requirements.txt

2. Configure Claude Desktop:

   Step 1: Locate your Claude Desktop config file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

   • Windows: %APPDATA%\Claude\claude_desktop_config.json

   • Linux: ~/.config/Claude/claude_desktop_config.json

   If the file doesn't exist, create it with {} as the initial content.

   Step 2: Add the MCP server configuration:

   For macOS/Linux:

   {
     "mcpServers": {
       "beatport-scraper": {
         "command": "/absolute/path/to/beatport-scraper/venv/bin/python3",
         "args": [
           "/absolute/path/to/beatport-scraper/src/mcp/server.py"
         ]
       }
     }
   }

   For Windows with WSL:

   {
     "mcpServers": {
       "beatport-scraper": {
         "command": "wsl",
         "args": [
           "-e",
           "/home/username/path/to/beatport-scraper/venv/bin/python3",
           "/home/username/path/to/beatport-scraper/src/mcp/server.py"
         ]
       }
     }
   }

   Important Notes:

   • Replace /absolute/path/to/beatport-scraper with your actual project path

   • For WSL, use Linux paths (e.g., /home/username/...)

   • Paths must be absolute, not relative

3. Test the configuration (optional):

   Before restarting Claude Desktop, test that the MCP server works:

   On macOS/Linux:

   /path/to/beatport-scraper/venv/bin/python3 /path/to/beatport-scraper/src/mcp/server.py

   On Windows with WSL:

   wsl -e /home/username/path/to/beatport-scraper/venv/bin/python3 /home/username/path/to/beatport-scraper/src/mcp/server.py

   The server should start and show a FastMCP banner. Press Ctrl+C to stop it.

4. Restart Claude Desktop:

   • Fully quit Claude Desktop (don't just close the window)

   • On Windows: Right-click the system tray icon and select "Quit" or use Task Manager

   • On macOS: Right-click the dock icon and select "Quit"

   • Wait a few seconds, then relaunch Claude Desktop

5. Use it in Claude Desktop:

   Once configured, simply ask Claude about Beatport tracks:

   • "Get metadata for this Beatport track: https://www.beatport.com/track/junin-shane-robinson-remix/7226500"

   • "Extract the audio preview URL from https://www.beatport.com/track/never-get-enough/15766697"

   Claude will automatically use the MCP server to extract track information including title, preview audio URL, and cover image.

Testing the MCP Server

Test the MCP server independently before configuring Claude Desktop:

# Run the test suite
python tests/test_mcp.py

# Or test the MCP stdio protocol directly
python tests/test_mcp_stdio.py

# Or run the server directly (press Ctrl+C to stop)
python src/mcp/server.py

Troubleshooting

Claude Desktop doesn't see the MCP server:

• Ensure you fully quit and restarted Claude Desktop (not just closed the window)

• Check that paths in the config are absolute and correct

• On Windows, verify WSL is working: wsl echo "test"

• Test the command manually before adding to Claude Desktop config

Server fails to start:

• Verify dependencies are installed: pip install -r requirements_mcp.txt

• Check Python path is correct: which python3 (in WSL/Linux) or where python (Windows)

• Ensure the virtual environment is activated when testing

Project Structure

.
├── Dockerfile             # Container configuration for Lambda
├── Makefile               # Common commands for testing and deployment
├── README.md              # This file
├── package.json           # Node.js dependencies (Serverless plugins)
├── requirements_prod.txt  # Python dependencies
├── requirements_mcp.txt   # MCP server dependencies
├── serverless.yml         # Serverless Framework configuration
├── src/                   # Source code
│   ├── handler.py         # Lambda handler function
│   └── mcp/               # MCP server implementations
│       └── server.py      # FastMCP server
└── tests/                 # Test files
    ├── events/
    │   └── beatport.json       # Sample event for testing
    ├── test_invoke_lambda.py   # Lambda invocation script
    ├── test_invoke_api.py      # API HTTP request script
    └── test_mcp_stdio.py       # MCP server test suite

Configuration

Lambda Function Settings

The function is configured with the following settings (defined in serverless.yml):

• Runtime: Python 3.11 (via Docker container)

• Timeout: 120 seconds

• Memory: 1024 MB

• Ephemeral Storage: 512 MB

• Platform: linux/amd64

Serverless Plugins

• serverless-prune-plugin: Automatically removes old Lambda versions (keeps last 2)

• serverless-dotenv-plugin: Loads environment variables from .env files

Troubleshooting

Docker Issues

If you encounter Docker-related errors during deployment:

# Ensure Docker is running
docker ps

# Build image locally to test
docker build -t beatport-test .

AWS Credentials

Ensure your AWS credentials are properly configured:

aws configure list

Lambda Timeout

If tracks are timing out, increase the timeout in serverless.yml:

functions:
  beatport:
    timeout: 180  # Increase from 120 to 180 seconds