spotify-direct-mcp
A FastMCP server that exposes Spotify's catalog and user context as tools for Claude, enabling track search, audio features, artist discography, recommendations, currently playing, and playlist creation.
README
spotify-direct-mcp
A FastMCP server that exposes Spotify as a set of tools for Claude.
Ask Claude things like:
- "What's the full discography of Sade?"
- "What key and BPM is 'Isn't She Lovely' in?"
- "Recommend 20 tracks with the same vibe as Stevie Wonder — high energy, soulful"
- "What am I listening to right now?"
- "Create a private playlist called 'Late Night Drive' from those recommendations"
Tools
| Tool | Description | Auth required |
|---|---|---|
search_tracks |
Search the Spotify catalog | No |
get_track_details |
Audio features (BPM, key, energy, danceability) + metadata | No |
get_artist_discography |
Full album/track list for an artist | No |
get_recommendations |
Mood/seed-based track recommendations | No |
get_currently_playing |
What's playing on your account right now | Yes |
create_playlist |
Create a playlist in your account | Yes |
Note: As of late 2024, Spotify restricts the
audio-featuresandrecommendationsendpoints to apps approved for "Extended Quota Mode". If your app doesn't have that approval,get_track_detailswill still return track metadata butaudio_featureswill contain an explanatory message instead of BPM/key data, andget_recommendationswill return anerrorfield with the same explanation. Request access at https://developer.spotify.com/documentation/web-api/concepts/quota-modes.
Prerequisites
- Python 3.10+ (required by FastMCP and the underlying MCP SDK — see below if you're not sure what you have)
- A Spotify account (free or premium)
- A Spotify Developer app (developer.spotify.com/dashboard)
Checking your Python version
python3 --version
If this prints 3.10.x, 3.11.x, 3.12.x, or 3.13.x, you're good — skip to Step 1.
If it prints 3.9.x or lower (common on macOS, which ships an old system Python),
you need to install a newer Python before creating the virtual environment in
Step 2. pip install -r requirements.txt will fail with
Could not find a version that satisfies the requirement fastmcp>=2.0 if you try to
use Python 3.9 or earlier.
Installing Python 3.10+
macOS (Homebrew):
brew install python@3.12
This installs a separate python3.12 binary alongside your system Python — it won't
replace or break anything else. Verify with:
python3.12 --version
macOS (no Homebrew): Download the installer from
python.org/downloads and run it. After
installing, use python3.12 (or whichever version you installed) in place of
python3 below.
Windows: Download the installer from python.org/downloads, run it, and check "Add python.exe to PATH" during install. Verify with:
python --version
Linux (Debian/Ubuntu):
sudo apt update
sudo apt install python3.12 python3.12-venv
Using pyenv (any OS, if you manage multiple Python versions):
pyenv install 3.12.7
pyenv local 3.12.7 # sets this version for the spotify-direct-mcp directory
Once you have a 3.10+ interpreter available, use that binary (e.g. python3.12)
instead of python3 when creating the virtual environment in Step 2.
Step 1: Spotify Developer Setup
- Go to developer.spotify.com/dashboard
- Click Create app
- Fill in:
- App name:
spotify-direct-mcp - App description: anything
- Redirect URI:
http://localhost:8888/callback - Check: Web API
- App name:
- Click Save
- Copy your Client ID and Client Secret from the app settings
Step 2: Local Setup
# Clone or copy the project
cd spotify-direct-mcp
# Create virtual environment using Python 3.10+
# If `python3 --version` showed 3.10+, use python3 below.
# Otherwise, substitute the 3.10+ binary you installed above (e.g. python3.12).
python3 -m venv .venv
source .venv/bin/activate # Mac/Linux
# .venv\Scripts\activate # Windows
# Confirm the venv is using Python 3.10+
python --version
# Install dependencies
pip install -r requirements.txt
# Copy env template and fill in your credentials
cp .env.example .env
If pip install -r requirements.txt fails with
Could not find a version that satisfies the requirement fastmcp>=2.0, your venv was
created with Python <3.10. Delete it and recreate using a 3.10+ binary:
rm -rf .venv
python3.12 -m venv .venv # use whichever 3.10+ binary you installed
source .venv/bin/activate
pip install -r requirements.txt
Edit .env:
SPOTIFY_CLIENT_ID=your_client_id_here
SPOTIFY_CLIENT_SECRET=your_client_secret_here
SPOTIFY_REDIRECT_URI=http://localhost:8888/callback
Step 3: OAuth Setup (one-time)
Tools that access your personal account (get_currently_playing, create_playlist) require
a one-time OAuth authorization:
python oauth_setup.py
This will:
- Open a browser window to Spotify's auth page
- Ask you to authorize the app
- Redirect you to
localhost:8888/callback(the page won't load — that's fine) - Ask you to paste the full redirect URL
- Cache your token to
.spotify_cache
You only need to do this once. The token auto-refreshes.
Step 4: Run Locally
Option A: stdio (for Claude Desktop)
python server.py
# or explicitly:
python server.py stdio
Option B: SSE (for Railway / remote clients)
python server.py sse
# Server starts on http://localhost:8000
Step 5: Connect to Claude Desktop
Add this to your Claude Desktop config file:
Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"spotify-direct-mcp": {
"command": "/absolute/path/to/.venv/bin/python",
"args": ["/absolute/path/to/spotify-direct-mcp/server.py"],
"env": {
"SPOTIFY_CLIENT_ID": "your_client_id_here",
"SPOTIFY_CLIENT_SECRET": "your_client_secret_here",
"SPOTIFY_REDIRECT_URI": "http://localhost:8888/callback"
}
}
}
}
Replace /absolute/path/to/ with your actual paths.
Restart Claude Desktop after saving. You should see spotify-direct-mcp in the tools list.
Step 6: Deploy to Railway
First-time setup
# Install Railway CLI
npm install -g @railway/cli
# Login
railway login
# Initialize project
railway init
# Set environment variables
railway variables set SPOTIFY_CLIENT_ID=your_client_id_here
railway variables set SPOTIFY_CLIENT_SECRET=your_client_secret_here
railway variables set SPOTIFY_REDIRECT_URI=https://your-app.railway.app/callback
Update Redirect URI on Spotify
Before deploying, add your Railway URL as an allowed redirect URI in your Spotify app settings:
- Go to developer.spotify.com/dashboard
- Click your app → Edit Settings
- Add
https://your-app.railway.app/callbackto Redirect URIs - Save
Deploy
railway up
Railway reads railway.toml and automatically:
- Detects Python via Nixpacks
- Installs
requirements.txt - Runs
python server.py sse - Sets
PORTenvironment variable
Get your deployed URL
railway domain
# Returns something like: https://spotify-direct-mcp-production.up.railway.app
Connect Claude to your Railway deployment
Once deployed, your MCP server is accessible via SSE at:
https://your-app.railway.app/sse
Add this to your Claude Desktop config:
{
"mcpServers": {
"spotify-direct-mcp": {
"url": "https://your-app.railway.app/sse"
}
}
}
Important: OAuth on Railway
get_currently_playing and create_playlist require an OAuth token tied to your Spotify account.
The .spotify_cache file generated locally is not automatically available on Railway. You have two options:
Option A (Recommended for personal use): Complete OAuth locally, then set the token contents as a Railway environment variable:
# After running oauth_setup.py locally:
cat .spotify_cache
# Copy the JSON output, then:
railway variables set SPOTIFY_TOKEN_CACHE='{"access_token": "...", ...}'
Then update server.py to read from this env var if the cache file doesn't exist — or simply use the server for catalog tools only (search, details, discography, recommendations) which don't need user auth.
Option B: Use a token refresh endpoint pattern (more complex, not scaffolded here).
For the interview demo, Option A or catalog-only is perfectly sufficient.
Project Structure
spotify-direct-mcp/
├── server.py # FastMCP server — all tools live here
├── oauth_setup.py # One-time OAuth flow helper
├── requirements.txt # Python dependencies
├── railway.toml # Railway deployment config
├── .env.example # Environment variable template
├── .env # Your actual credentials (gitignored)
├── .spotify_cache # OAuth token cache (gitignored)
└── README.md # This file
Example Prompts for Claude
Once connected, try these:
Search for "Isn't She Lovely" and give me the BPM and musical key.
What's Sade's full discography sorted by year?
Recommend 15 tracks similar to Anita Ward with high danceability and moderate energy.
What am I currently listening to? What key is it in?
Create a private playlist called "Sunday Morning Soul" from these track URIs: [paste URIs]
Search for top tracks by Stevie Wonder from the 1970s.
Architecture Notes
- Client Credentials flow is used for all catalog tools (no user login required)
- OAuth PKCE flow is used for user-context tools (currently playing, create playlist)
- FastMCP handles the MCP protocol — all tools are decorated with
@mcp.tool() - Transport is switchable:
stdiofor local/Claude Desktop,ssefor Railway/remote - Railway auto-detects Python via Nixpacks — no Dockerfile needed
Built With
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。