yt-dj-mcp

yt-dj-mcp

Syncs YouTube Music liked songs, analyzes them for DJ metadata like BPM and key, and enables creating playlists from previews.

Category
Visit Server

README

yt-dj-mcp

Local YouTube Music DJ crate MCP server.

The server syncs liked songs from YouTube Music, stores approximate DJ metadata such as BPM, key, Camelot key, and genre, then creates playlists only from saved previews.

Who This Is For

Use this if you have a large YouTube Music liked-song library and want a local, agent-friendly way to turn it into DJ crates. The workflow is deliberately preview-first: inspect candidates locally, then create playlists only after you approve a saved preview.

What It Can Do

  • Sync YouTube Music liked songs into local SQLite.
  • Analyze short local audio sections with yt-dlp, ffmpeg, and Essentia.
  • Store BPM, musical key, Camelot key, confidence, and source.
  • Search tracks by BPM/key/genre.
  • Save playlist previews before writing to YouTube Music.
  • Create YouTube Music playlists from reviewed previews.

Safety

  • Uses OAuth by default.
  • Stores secrets in ~/.config/yt-dj-mcp/.
  • Never prints credential file contents.
  • Creates playlists only from saved previews.
  • Browser/HAR auth is an explicit fallback and stores session headers in browser.json; keep it local and never commit it.
  • Runtime state is local-only. Do not publish library.sqlite, HAR files, browser headers, OAuth tokens, or downloaded audio.

Quick Start

git clone https://github.com/YOUR_USERNAME/yt-dj-mcp.git
cd yt-dj-mcp
python -m venv .venv
. .venv/bin/activate
pip install -e ".[dev,audio]"
pytest -q

Add the MCP server to your client:

[mcp_servers.yt_dj_mcp]
command = "/absolute/path/to/yt-dj-mcp/.venv/bin/yt-dj-mcp"
args = ["serve"]

OAuth Setup

Create a Google Cloud OAuth client that can be used by ytmusicapi, then run:

yt-dj-mcp setup-auth
yt-dj-mcp auth-status

If OAuth token creation succeeds but YouTube Music requests return HTTP 400, use the explicit local browser fallback:

ytmusicapi browser --file ~/.config/yt-dj-mcp/browser.json
chmod 600 ~/.config/yt-dj-mcp/browser.json
yt-dj-mcp auth-status

If copying request headers from DevTools is unreliable, export a Chrome Network HAR and import it:

yt-dj-mcp import-har /path/to/music.youtube.com.har
yt-dj-mcp auth-status

browser.json contains browser session headers. Keep it local and never commit it.

Commands

yt-dj-mcp setup-auth
yt-dj-mcp auth-status
yt-dj-mcp import-har /path/to/music.youtube.com.har
yt-dj-mcp enrich-tracks --limit 25
yt-dj-mcp reset-auth
yt-dj-mcp serve

MCP tools exposed by the server:

  • auth_status
  • setup_auth_instructions
  • sync_liked_songs
  • list_liked_songs
  • enrich_tracks
  • find_dj_candidates
  • create_playlist_preview
  • get_playlist_preview
  • create_youtube_music_playlist
  • update_track_metadata

Audio Metadata Enrichment

enrich-tracks fills missing BPM/key/Camelot metadata by downloading a short audio section with yt-dlp, analyzing it with Essentia, then caching the analysis under ~/.config/yt-dj-mcp/audio-metadata-cache/.

Required local commands/packages:

ffmpeg -version
python -m yt_dlp --version
python -c "import essentia; print(essentia.__version__)"

The command is best run in small batches:

yt-dj-mcp enrich-tracks --limit 10
yt-dj-mcp enrich-tracks --limit 50

If yt-dlp or Essentia is missing, enrichment will leave tracks in the missing count instead of crashing the server. Use this only for music you are allowed to process locally; downloading streamed audio may be restricted by the service's terms.

First Crate Workflow

  1. Run yt-dj-mcp setup-auth.
  2. Run yt-dj-mcp auth-status.
  3. Add the MCP server to Codex or Claude.
  4. Call sync_liked_songs.
  5. Run yt-dj-mcp enrich-tracks --limit 25 or call MCP enrich_tracks.
  6. Call find_dj_candidates.
  7. Call create_playlist_preview.
  8. Review with get_playlist_preview.
  9. Create the YouTube Music playlist with create_youtube_music_playlist.

Suggested Agent Prompt

After installing the MCP server, try:

Sync my YouTube Music liked songs, enrich 50 tracks with BPM/key metadata, then
create 12-track DJ playlist previews grouped by compatible BPM and Camelot key.

Review previews before asking the agent to create real YouTube Music playlists.

Limitations

  • BPM/key values are estimates. Use your ears before playing out.
  • YouTube Music auth can be brittle. OAuth is preferred, while browser/HAR auth is a local fallback when OAuth does not work.
  • Audio analysis depends on ffmpeg, yt-dlp, and Essentia availability.
  • This project does not manage playback and does not provide destructive library operations.

License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured