spotify-llm-mcp

Control Spotify and build LLM-reasoned playlists from any MCP client (Claude Code, Claude Desktop, Cursor, …).

It wraps the spotify_player CLI as the control plane, so it needs no Spotify Developer app and no OAuth of its own — it reuses the session you already logged into. The recommendation engine is the calling LLM itself: it reasons over your real playlist tracks and the server resolves its picks to Spotify tracks via search. (Spotify removed its audio-features/recommendations endpoints for new apps in Nov 2024 — this design sidesteps that entirely.)

Why this design

• Zero new credentials. spotify_player already holds your session; this server just shells out to it.

• Immune to Spotify Web-API deprecations. Control flows through librespot, not api.spotify.com.

• The LLM is the recsys. Its music knowledge replaces Spotify's dead recommendation endpoints; the server turns song names into playable Spotify tracks.

• Portable. One MCP server, usable by every MCP-speaking client.

Related MCP server: Spotify MCP Server

Requirements

• spotify_player installed, authenticated once, and running as an active device. Spotify Premium is required for playback control.

• Python ≥ 3.10.

Install

git clone https://github.com/rachittshah/spotify-llm-mcp
cd spotify-llm-mcp
uv pip install -e .

Register with Claude Code:

claude mcp add spotify -- spotify-llm-mcp

Or in settings.json / claude_desktop_config.json:

{ "mcpServers": { "spotify": { "command": "spotify-llm-mcp" } } }

Tools

Control: now_playing · control (play/pause/next/previous/shuffle/repeat/volume/seek) · play · search · list_devices · switch_device · get_queue

Recsys inputs & delivery: list_playlists · get_playlist_tracks · taste_profile · resolve_tracks · create_playlist_from_tracks · append_to_playlist

Features (MCP prompts / slash-commands)

• /recommend-from-playlist <name> — read a playlist, let the LLM extend it in-vibe, build & play the result.

• /vibe-playlist <description> — "late-night coding, downtempo instrumental" → a fresh playlist.

• /more-like-this — recommendations seeded from what's playing now.

• /my-taste — a summary of your listening profile.

How the recsys works

LLM client ──► get_playlist_tracks("On Repeat")      # real tracks, via spotify_player
   │  (LLM reasons over them using its music knowledge)
   └─► create_playlist_from_tracks(name, [{title, artist}, ...])
          │  server resolves each name → Spotify track via search
          └─► play(new_playlist_uri)                 # on your spotify_player device

Claude Code skill

This repo also ships a Claude Code skill (skill/spotify-dj/) that teaches Claude the recsys workflows on top of the MCP tools. Install it:

cp -r skill/spotify-dj ~/.claude/skills/spotify-dj

Then in Claude Code: "recommend music like my On Repeat playlist" or /spotify-dj.

Known limitations

• Playback control needs Spotify Premium. Free accounts can read state but not control it.

• spotify_player's search errors on some queries (an upstream JSON-deserialization bug — exit 0 but non-JSON output). The recsys is resilient to this: an unresolvable pick is skipped and reported under unresolved, never aborting the batch.

• Delivery is a playlist, not an ephemeral queue — spotify_player's CLI has no "add to queue" verb, so recommendations are delivered as a (revisitable) playlist.

Development

uv pip install -e ".[dev]"
.venv/bin/python -m pytest            # unit tests (no Spotify needed)
.venv/bin/python scripts/smoke_live.py  # end-to-end against a live spotify_player
.venv/bin/python scripts/smoke_mcp.py    # MCP-protocol smoke over stdio

License

MIT © Rachitt Shah