Spotify MCP Server

A FastMCP server that provides provides access to Spotify's music data through the Spotify Web API.

🎵 Features

• Search Tracks: Find songs by name, artist, or lyrics

• Search Artists: Discover artists and get their information

• Artist Top Tracks: Get the most popular tracks for any artist

Note: The Recommendations API is no longer available for new Spotify apps as of November 27, 2024. See Spotify's announcement for more details.

🚀 Quick Start

Prerequisites

• Python 3.8+

• Spotify Developer Account

• Spotify App credentials (Client ID and Client Secret)

Local Development

1. Clone and navigate to the repository:

   git clone <your-repo-url> cd mcp-spotify

2. Create a virtual environment:

   python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up Spotify API credentials:

   export SPOTIFY_CLIENT_ID="your_client_id" export SPOTIFY_CLIENT_SECRET="your_client_secret"

5. Run the server:

   python src/server.py

The server will start on http://localhost:8000 with the MCP endpoint at http://localhost:8000/mcp.

🔑 Getting Spotify API Credentials

1. Go to

2. Log in with your Spotify account

3. Click "Create App"

4. Fill in the app details:

   • App name: Your MCP Server (or any name you prefer)

   • App description: MCP server for Spotify integration

   • Website: https://spotify-mcp.onrender.com (use your deployed URL)

   • Redirect URI: https://spotify-mcp.onrender.com (use your deployed URL)

5. Select "Web API" when asked about APIs/SDKs

6. Click "Save"

7. Copy your

Note: Use your deployed Render URL (not localhost) for the redirect URI since Spotify requires HTTPS for security.

🚢 Deployment

Deploy to Render

Steps:

1. Click the "Deploy to Render" button below (or go to render.com and sign up/login)

2. Connect your GitHub account to Render (if you haven't already)

3. Create a new Web Service:

   • Connect this repository

   • Name: spotify-mcp

   • Environment: Python 3

   • Plan: Free

   • Build Command: pip install -r requirements.txt

   • Start Command: python src/server.py

4. Set environment variables:

   • Go to your Render service dashboard

   • Click on "Environment" tab

   • Add:

     • SPOTIFY_CLIENT_ID = your_client_id

     • SPOTIFY_CLIENT_SECRET = your_client_secret

   • Click "Save Changes"

5. Deploy!

Note: On Render's free tier, services go idle after ~15 minutes of inactivity and may require a manual "Deploy" to wake or to pick up the latest commit. Unlike Vercel, pushes do not auto-deploy by default.

Your server will be available at https://spotify-mcp.onrender.com/mcp

🎯 Poke Integration

1. Go to poke.com/settings/connections

2. Add the MCP URL: https://spotify-mcp.onrender.com/mcp

3. Give it a name like "Spotify"

4. Try: "Can you use the Spotify MCP to search tracks for 'Bohemian Rhapsody'?"

References

• Based on the Interaction MCP server template: MCP Server Template

• Discovered via Interaction’s HackMIT challenge: Interaction HackMIT Challenge

🛠️ Available Tools

search_tracks(query, limit=10)

Search for tracks on Spotify.

Parameters:

• query (string): Search query (song name, artist, lyrics, etc.)

• limit (int, optional): Number of results to return (1-50, default: 10)

Example:

search_artists(query, limit=10)

Search for artists on Spotify.

Parameters:

• query (string): Search query (artist name, genre, etc.)

• limit (int, optional): Number of results to return (1-50, default: 10)

Example:

get_artist_top_tracks(artist_id, market="US")

Get the top tracks for a specific artist.

Parameters:

• artist_id (string): Spotify artist ID

• market (string, optional): Market code (default: "US")

Example:

🧪 Testing

Test the server locally:

📝 Notes

• Rate Limiting: The Spotify API has rate limits. The server includes automatic token refresh.

• Authentication: Uses Spotify's Client Credentials flow (no user login required).

• Data Format: All responses are in JSON format for easy integration.

• Error Handling: Comprehensive error handling with descriptive error messages.