Music AI Agent — MCP Server - specialized in classical music recognition

A Python MCP server exposing music analysis tools: catalog search, track metadata, local tempo/key/energy detection, recommendations, and optional song recognition.

Why it's built this way

Spotify locked down its audio-features, audio-analysis, recommendations, and related-artists endpoints for all new apps in November 2024, and hasn't reopened them. So:

• Search + track metadata → still Spotify (works great).

• Tempo / key / energy → computed locally with librosa on an actual audio file, instead of asking Spotify for numbers it no longer gives out.

• Recommendations → Last.fm's track.getSimilar, which is still free and public.

• "What song is this?" → a separate problem (audio fingerprinting), done via AudD's API — optional, only needed if you want that specific feature.

Related MCP server: Spotify MCP Server

Setup

cd music-mcp-agent
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env

Fill in .env:

• SPOTIFY_CLIENT_ID / SPOTIFY_CLIENT_SECRET — required for search & metadata. Create an app at https://developer.spotify.com/dashboard (takes ~2 minutes, no approval wait — client-credentials apps don't need extended access).

• LASTFM_API_KEY — required for get_similar_tracks. Free, instant: https://www.last.fm/api/account/create

• AUDD_API_KEY — optional, only for identify_song. Free tier at https://audd.io/

Run it

python server.py

Connect it to Claude Desktop

Add to your Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "music-agent": {
      "command": "/absolute/path/to/venv/bin/python",
      "args": ["/absolute/path/to/music-mcp-agent/server.py"]
    }
  }
}

Restart Claude Desktop. The tools will show up under the 🔌 icon.

Tools

Classical music: why two extra steps

Spotify and Last.fm treat a classical recording as one flat string — "Symphony No. 5 in C Minor, Op. 67: I. Allegro con brio" — with composer, work, and performing orchestra mashed together inconsistently. Neither is a good source for "what's this piece" or "who's performing it live," so:

1. resolve_classical_work uses MusicBrainz (free, no key needed beyond a descriptive User-Agent) to split that string into composer + canonical work title.

2. find_live_performances takes that composer/work/orchestra and searches Songkick's events database for upcoming concerts, optionally filtered by city.

Typical flow: search_music → resolve_classical_work on the result → find_live_performances with the resolved composer or orchestra name.

Songkick access isn't instant — you have to apply at https://www.songkick.com/developer and approval is manual (can take a couple of weeks), so plan for that lead time before you rely on find_live_performances.

Note on Bachtrack: it's the most classical-specific concert listings site, but it has no public developer API — only a searchable website — so it isn't wired in here.

Next steps to extend this

• Add caching (the Spotify token, repeated searches, MusicBrainz lookups) with something like diskcache — MusicBrainz in particular rate-limits to ~1 request/second per the terms of their free API.

• Add a create_playlist tool if you get user-auth (Authorization Code flow) working instead of client-credentials — needed for anything that writes to a user's account.

• Swap analyze_audio_file to batch-process a folder for a full library scan.

• If AudD's free tier is too limited, ACRCloud is the other common choice for fingerprinting, similar integration shape.

• Once Songkick access comes through, consider adding a subscribe_orchestra tool that periodically checks an orchestra's calendar and only surfaces new additions — more useful than re-querying the full calendar each time.

Structure: music-mcp-agent/ ├── config.py # loads all env vars, once ├── server.py # MCP registration only ├── clients/ │ ├── init.py # marks clients/ as a Python package │ ├── spotify.py # search + track info │ ├── audio_analysis.py # local librosa tempo/key/energy │ ├── lastfm.py # similar tracks │ ├── audd.py # song recognition │ ├── musicbrainz.py # classical work resolution │ └── songkick.py # live performances ├── requirements.txt ├── .env.example └── README.md