MCP server

FreqBlog Music Metadata Official

com.freqblog/music-metadata

Checked 28 Oct 2026, 09:03 UTC · Connected to it and listed its tools

Audio features + harmonic set-building for tracks by name/ISRC. Spotify audio-features replacement.

At a glance

Type

MCP server

Publisher

freqblog.com (verified by the MCP Registry)

Status

Working at last check

Tools

12

Token cost

About 7,056 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

28 Oct 2026, 09:03 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http music-metadata https://mcp.freqblog.com/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.music-metadata]
url = "https://mcp.freqblog.com/mcp"

Gemini CLI

{"mcpServers":{"music-metadata":{"httpUrl":"https://mcp.freqblog.com/mcp"}}}

Cursor

{"mcpServers":{"music-metadata":{"url":"https://mcp.freqblog.com/mcp"}}}

VS Code

{"servers":{"music-metadata":{"type":"http","url":"https://mcp.freqblog.com/mcp"}}}

Antigravity

agy mcp add music-metadata https://mcp.freqblog.com/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"music-metadata","server_url":"https://mcp.freqblog.com/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"music-metadata","server_url":"https://mcp.freqblog.com/mcp"}

Tools

• build_setlist ~387 tokens
  Order a crate of 2-100 catalog tracks into a beat-matched DJ set that follows an energy arc, keeping each consecutive transition harmonically and tempo-smooth. `arc` is one of peak_time (default — builds to a peak then eases), warmup, cooldown, or flat. Returns the `arc`, `count`, an ov
• find_compatible_keys ~242 tokens
  Given a Camelot key (e.g. "8A", "12B"), return the harmonically compatible keys for DJ mixing — the same key, the relative major/minor, and the adjacent +/-1 keys on the Camelot wheel. With `extended=true` also returns the +7/-7 energy-boost / energy-drop keys. Pure music theory — no cat
• find_tracks_by_bpm ~260 tokens
  Find catalog tracks near a target tempo. Returns tracks whose BPM is within +/-`tolerance` of `bpm`, ordered by closeness then by an internal catalogue ordering key (not an audience metric) — useful for DJ set planning, workout playlists, or tempo-matching. Each returned track carries full a
• find_tracks_by_key ~238 tokens
  Find catalog tracks in a given musical key — for harmonic mixing and key-locked playlists. `key` accepts Camelot ("8A"), Open Key ("1m"), or a key name ("A-Minor", "F#-Major"). Returns tracks ordered by an internal catalogue ordering key (not an audience metric), each with full audio feature
• get_audio_features ~978 tokens
  Get audio features for ONE track — BPM, musical key (name + Camelot + Open Key), energy, danceability, valence, acousticness, loudness, mood, mood_vector, genre, time signature, duration and more. This is the drop-in replacement for Spotify's deprecated /audio-features endpoint. Provide
• get_audio_features_batch ~821 tokens
  Get audio features for MANY tracks in one call (up to 50 processed) — ideal for analysing a whole playlist at once. Identify each item by name (`track`/`artist`), by `isrc` (matched exactly first — best for CJK / K-pop / niche tracks whose fuzzy name-match misses), or both (ISRC first, n
• get_recommendations ~1291 tokens
  Recommended tracks for one or more seed tracks — the drop-in for Spotify's removed GET /v1/recommendations. Blends up to 5 catalog seed tracks into a single point in audio-feature space and returns the nearest catalogue tracks, RE-RANKED by genre affinity (so a feature-close cross-genre
• get_related_artists ~310 tokens
  Artists related to a seed artist — the drop-in for Spotify's removed GET /v1/artists/{id}/related-artists. No artist graph exists, so we derive one: build the seed artist's track-vector centroid, take its nearest catalogue tracks, aggregate by artist (each scored on its top-3 track simil
• score_transition ~373 tokens
  Score how well one catalog track mixes into another (0-100) — the pairwise DJ transition score no raw key/BPM API gives you. Combines Camelot-wheel key compatibility, octave-aware BPM proximity (half/double-time counts as a match), and energy smoothness. Returns the overall `score`, per
• search_catalog ~520 tokens
  Full-text search the catalog by any mix of track / artist / album tokens. Use this to resolve a fuzzy, partial, or misspelled name into concrete tracks BEFORE calling get_audio_features. Each hit carries `chart_peak`: the best chart position we hold, 1-100 with 100 = a number-one. `
• suggest_next_track ~672 tokens
  Given a seed track, return the top-N catalog tracks to play NEXT, ranked by transition score. Each suggestion carries the same `score`, per-component scores and human `reason` as score_transition (e.g. "11B->11B same key, 118->117 BPM (-0.29), energy +0.12"), plus its `genre` and `genre_
• tag_track ~964 tokens
  Get a compact, HONESTLY-LABELLED tag list for a track — energy / danceability / valence / acousticness / instrumentalness (instrumental vs vocal, judged on the 30-second preview), plus a mood tag and a broad genre tag. It is a tag-shaped projection of the same open-data analysis get_audi

Browse

• Website monitoring
• Music and audio

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