Audio features + harmonic set-building for tracks by name/ISRC. Spotify audio-features replacement.
Abgeleitet aus den Transporten, die dieser Eintrag deklariert (streamable-http). Ein Client, der hier nicht steht, ist damit nicht ausgeschlossen — Forge kann ihn nur nicht bestätigen.
Die Verifizierung bestätigt die Identität des Publishers (die Inhaberschaft am Repo), nicht die Sicherheit des Codes. Der Sicherheits-Scan deckt bekannte CVEs und verdächtige Installationsskripte ab.
Aus einem echten MCP-Handshake initialize → tools/list gegen den deklarierten Endpunkt gelesen. Es wurde nie ein Tool aufgerufen — tools/list ist der lesende Introspektionsaufruf, den das Protokoll dafür vorsieht. Es spiegelt wider, was der Server in diesem Moment angeboten hat; ein gehosteter Endpunkt ist an keine Version gebunden und kann sich ohne Ankündigung ändern.
https://mcp.freqblog.com/mcp12 Tools · 2825 msget_audio_featuresGet audio features for ONE track — BPM, musical key (name + Camelot + Open Key),
energy, danceability, valence, acousticness, instrumentalness, liveness, speechiness,
loudness, mood, mood_vector, genre, time signature, duration and more.
This is the drop-in replacement for Spotify's deprecated /audio-features endpoint.
Provide AT LEAST ONE identifier — if you know several, send them all rather than
choosing; they resolve by precedence (`track` > `isrc` > `mbid` > `spotify_id`) and
the rest are ignored:
- `track` (optionally with `artist`) — e.g. track="Blinding Lights", artist="The Weeknd".
- `isrc` — e.g. "USUM71900001".
- `mbid` — a MusicBrainz recording UUID.
- `spotify_id` — a Spotify track ID, URI, or URL (resolves only the <1% of the
catalog already mapped to a Spotify ID; prefer `track`/`isrc` for full coverage).
Returns a JSON object of features. Some feature fields may be null for tracks resolved
via the fallback catalogs (only audio-derived values are present for fully analysed
tracks). If a track name is not yet in the catalog, the API holds the request during the
on-demand ingest and usually returns the fully analysed track inline in this same call;
only if the ingest runs long does it fall back to a queued response you can re-poll
shortly (~15s). If the track turns out not to be on any streaming source we can analyse,
you get a definitive not-found instead — that verdict is terminal for ~7 days, so don't
retry it. If you only have a fuzzy or partial name, call search_catalog first to
find the exact track.Get audio features for ONE track — BPM, musical key (name + Camelot + Open Key), energy, danceability, valence, acousticness, instrumentalness, liveness, speechiness, loudness, mood, mood_vector, genre, time signature, duration and more. This is the drop-in replacement for Spotify's deprecated /audio-features endpoint. Provide AT LEAST ONE identifier — if you know several, send them all rather than choosing; they resolve by precedence (`track` > `isrc` > `mbid` > `spotify_id`) and the rest are ignored: - `track` (optionally with `artist`) — e.g. track="Blinding Lights", artist="The Weeknd". - `isrc` — e.g. "USUM71900001". - `mbid` — a MusicBrainz recording UUID. - `spotify_id` — a Spotify track ID, URI, or URL (resolves only the <1% of the catalog already mapped to a Spotify ID; prefer `track`/`isrc` for full coverage). Returns a JSON object of features. Some feature fields may be null for tracks resolved via the fallback catalogs (only audio-derived values are present for fully analysed tracks). If a track name is not yet in the catalog, the API holds the request during the on-demand ingest and usually returns the fully analysed track inline in this same call; only if the ingest runs long does it fall back to a queued response you can re-poll shortly (~15s). If the track turns out not to be on any streaming source we can analyse, you get a definitive not-found instead — that verdict is terminal for ~7 days, so don't retry it. If you only have a fuzzy or partial name, call search_catalog first to find the exact track.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
get_audio_features_batchGet 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, name as the fallback).
One bad entry never fails the batch. Items beyond the 50-per-call cap come back with
`found: false` and `backfill_status: "over_limit"`; an item missing BOTH `track` and
`isrc` comes back `"invalid_no_query"`. Neither is processed or charged — the response's
`skipped` field counts them, so split a long list into calls of <=50 and resubmit any
skipped rows.
Returns counts (`found` / `not_found` / `skipped`) plus a per-track `results` array, where
each entry's `result` is the same feature object as get_audio_features (or null when not
found), and `isrc` is echoed back. An item is billed only when it returns features or
queues an on-demand ingest; an ISRC/name with no match anywhere is free. For a single
track, use get_audio_features.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, name as the fallback). One bad entry never fails the batch. Items beyond the 50-per-call cap come back with `found: false` and `backfill_status: "over_limit"`; an item missing BOTH `track` and `isrc` comes back `"invalid_no_query"`. Neither is processed or charged — the response's `skipped` field counts them, so split a long list into calls of <=50 and resubmit any skipped rows. Returns counts (`found` / `not_found` / `skipped`) plus a per-track `results` array, where each entry's `result` is the same feature object as get_audio_features (or null when not found), and `isrc` is echoed back. An item is billed only when it returns features or queues an on-demand ingest; an ISRC/name with no match anywhere is free. For a single track, use get_audio_features.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
search_catalogFull-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.
Returns lightweight stubs (itunes_track_id, track_name, artist_name, album, etc.) ranked
by relevance — NOT audio features. Take the best match's track_name + artist_name and
pass them to get_audio_features, or reuse its itunes_track_id as a `track_id` seed for
discovery tools.
⚠ Each hit carries a `seedable` boolean. Only a hit with `seedable: true` can be used as
a seed for get_recommendations / suggest_next_track / build_setlist / score_transition —
those work off the similarity index, which holds only tracks we have analysed, and about
a quarter of the catalogue is not analysed yet. **Prefer the highest-ranked hit with
`seedable: true`.** Seeding with a `seedable: false` id returns a 404; if that track is
the one you want, call get_audio_features on it first to queue analysis, then retry.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. Returns lightweight stubs (itunes_track_id, track_name, artist_name, album, etc.) ranked by relevance — NOT audio features. Take the best match's track_name + artist_name and pass them to get_audio_features, or reuse its itunes_track_id as a `track_id` seed for discovery tools. ⚠ Each hit carries a `seedable` boolean. Only a hit with `seedable: true` can be used as a seed for get_recommendations / suggest_next_track / build_setlist / score_transition — those work off the similarity index, which holds only tracks we have analysed, and about a quarter of the catalogue is not analysed yet. **Prefer the highest-ranked hit with `seedable: true`.** Seeding with a `seedable: false` id returns a 404; if that track is the one you want, call get_audio_features on it first to queue analysis, then retry.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
find_tracks_by_bpmFind catalog tracks near a target tempo. Returns tracks whose BPM is within
+/-`tolerance` of `bpm`, ordered by closeness then popularity — useful for DJ set
planning, workout playlists, or tempo-matching. Each returned track carries full audio
features. To also constrain by musical key, combine with find_tracks_by_key.Find catalog tracks near a target tempo. Returns tracks whose BPM is within +/-`tolerance` of `bpm`, ordered by closeness then popularity — useful for DJ set planning, workout playlists, or tempo-matching. Each returned track carries full audio features. To also constrain by musical key, combine with find_tracks_by_key.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
find_tracks_by_keyFind 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 popularity, each with full audio features. To
discover which keys mix well with a given key first, use find_compatible_keys.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 popularity, each with full audio features. To discover which keys mix well with a given key first, use find_compatible_keys.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
find_compatible_keysGiven 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 catalog lookup and no quota cost. Pair with find_tracks_by_key
to then pull actual tracks in each compatible key.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 catalog lookup and no quota cost. Pair with find_tracks_by_key to then pull actual tracks in each compatible key.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
score_transitionScore 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-component scores (`harmonic`/`tempo`/`energy`), a `detail`
block (key_relation, both Camelot keys, both BPMs, bpm_delta, bpm_octave_matched, both
energies, energy_delta), and a one-line human `reason` (e.g. "8A->9A adjacent (+1), 126->128
BPM (+2), energy +0.04 — clean uplifting mix"). Both ids are catalog itunes_track_ids — get
them from search_catalog or the itunes_track_id field of a get_audio_features result. Costs
1 quota unit.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-component scores (`harmonic`/`tempo`/`energy`), a `detail` block (key_relation, both Camelot keys, both BPMs, bpm_delta, bpm_octave_matched, both energies, energy_delta), and a one-line human `reason` (e.g. "8A->9A adjacent (+1), 126->128 BPM (+2), energy +0.04 — clean uplifting mix"). Both ids are catalog itunes_track_ids — get them from search_catalog or the itunes_track_id field of a get_audio_features result. Costs 1 quota unit.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
suggest_next_trackGiven 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_relation` to the seed. GENRE-AWARE by default (cross_genre=auto): off-genre
picks that only coincidentally share the seed's key/BPM sink to the bottom — use
cross_genre=strict for same-genre-family only, or allow for the old harmonic-only ranking. It
is the seed's sonic neighbours re-ranked for a clean mix.
Returns `seed`, `count`, and a `suggestions` array of {track, score, components, reason}.
seed_track_id is a catalog itunes_track_id from search_catalog or a get_audio_features
result. Pair with build_setlist to order a whole crate. Costs 3 quota units.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_relation` to the seed. GENRE-AWARE by default (cross_genre=auto): off-genre picks that only coincidentally share the seed's key/BPM sink to the bottom — use cross_genre=strict for same-genre-family only, or allow for the old harmonic-only ranking. It is the seed's sonic neighbours re-ranked for a clean mix. Returns `seed`, `count`, and a `suggestions` array of {track, score, components, reason}. seed_track_id is a catalog itunes_track_id from search_catalog or a get_audio_features result. Pair with build_setlist to order a whole crate. Costs 3 quota units.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
build_setlistOrder 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 overall `flow_score` (0-100), the `tracks` in play order, the
per-step `transitions` ({from_index, to_index, score, reason}), and `omitted` (ids not found
in the catalog). Feed tracks[].itunes_track_id into a Rekordbox/Serato export to drop the set
straight into your DJ software. track_ids are catalog itunes_track_ids. Costs 5 quota units.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 overall `flow_score` (0-100), the `tracks` in play order, the per-step `transitions` ({from_index, to_index, score, reason}), and `omitted` (ids not found in the catalog). Feed tracks[].itunes_track_id into a Rekordbox/Serato export to drop the set straight into your DJ software. track_ids are catalog itunes_track_ids. Costs 5 quota units.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
get_recommendationsRecommended 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 track doesn't outrank same-genre picks).
Returns `seeds` (each {id, found}), `count`, and `tracks` (each {track, score,
genre_relation}; each track carries its `genre`). `genre_relation` is "same", "compatible"
(different but mixable family), "cross" (unrelated), or "unknown" (either side has no mapped
genre), measured against the PRIMARY seed — the first of your seed_tracks we could actually
use, so reordering seed_tracks changes it and a skipped seed never becomes the reference.
With a SINGLE seed the field is the ranking's own verdict, so it explains the order (same as
suggest_next_track). With SEVERAL seeds the ranking considers ALL of them while the label stays
relative to your primary seed, so a "cross" label on a multi-seed call does NOT mean the track
was pushed down — it may share a family with another of your seeds. `score` is the raw
audio-feature cosine similarity in [0,1]; genre affinity influences the ORDER, not the score,
so the list is NOT strictly score-descending.
Use cross_genre=strict to return same-genre-family tracks ONLY (off-genre dropped
server-side), or allow to disable the genre ranking. seed_tracks are catalog itunes_track_ids
from search_catalog or the itunes_track_id field of a get_audio_features result.
NO id? Pass `track` (+ optional `artist`) instead and we resolve the name to the best catalog
match and seed on it — the resolved track is echoed back as `seed_query`; seed_tracks wins if
both are given.
TUNING: `min`/`max` are HARD filters and `target` is a preference (nearer ranks higher,
nothing removed), over acousticness, danceability, duration_ms, energy, instrumentalness,
liveness, loudness, popularity, speechiness, tempo and valence. e.g. min={"tempo": 100},
max={"tempo": 130}, target={"energy": 0.8} for energetic 100-130 BPM tracks. When you tune,
the response adds a `filters` block saying what applied, how many tracks each bound removed
(`dropped_by`) and whether the bounds ran out of catalogue before `limit` (`limit_reached`)
— if the list comes back short, read that BEFORE assuming the catalogue is thin.
Costs 2 quota units.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 track doesn't outrank same-genre picks). Returns `seeds` (each {id, found}), `count`, and `tracks` (each {track, score, genre_relation}; each track carries its `genre`). `genre_relation` is "same", "compatible" (different but mixable family), "cross" (unrelated), or "unknown" (either side has no mapped genre), measured against the PRIMARY seed — the first of your seed_tracks we could actually use, so reordering seed_tracks changes it and a skipped seed never becomes the reference. With a SINGLE seed the field is the ranking's own verdict, so it explains the order (same as suggest_next_track). With SEVERAL seeds the ranking considers ALL of them while the label stays relative to your primary seed, so a "cross" label on a multi-seed call does NOT mean the track was pushed down — it may share a family with another of your seeds. `score` is the raw audio-feature cosine similarity in [0,1]; genre affinity influences the ORDER, not the score, so the list is NOT strictly score-descending. Use cross_genre=strict to return same-genre-family tracks ONLY (off-genre dropped server-side), or allow to disable the genre ranking. seed_tracks are catalog itunes_track_ids from search_catalog or the itunes_track_id field of a get_audio_features result. NO id? Pass `track` (+ optional `artist`) instead and we resolve the name to the best catalog match and seed on it — the resolved track is echoed back as `seed_query`; seed_tracks wins if both are given. TUNING: `min`/`max` are HARD filters and `target` is a preference (nearer ranks higher, nothing removed), over acousticness, danceability, duration_ms, energy, instrumentalness, liveness, loudness, popularity, speechiness, tempo and valence. e.g. min={"tempo": 100}, max={"tempo": 130}, target={"energy": 0.8} for energetic 100-130 BPM tracks. When you tune, the response adds a `filters` block saying what applied, how many tracks each bound removed (`dropped_by`) and whether the bounds ran out of catalogue before `limit` (`limit_reached`) — if the list comes back short, read that BEFORE assuming the catalogue is thin. Costs 2 quota units.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
get_related_artistsArtists 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 similarities so a prolific artist can't dominate) plus a
same-genre lift and a cross-genre penalty.
Returns `artist`, `count`, and `related` (each {artist_name, score, match_count,
sample_track_id}). Pass a sample_track_id straight to get_audio_features or
suggest_next_track. Costs 2 quota units.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 similarities so a prolific artist can't dominate) plus a same-genre lift and a cross-genre penalty. Returns `artist`, `count`, and `related` (each {artist_name, score, match_count, sample_track_id}). Pass a sample_track_id straight to get_audio_features or suggest_next_track. Costs 2 quota units.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
tag_trackGet a compact, HONESTLY-LABELLED tag list for a track — energy / danceability / valence /
acousticness / instrumentalness, plus a mood tag and a broad genre tag. It is a tag-shaped
projection of the same open-data analysis get_audio_features returns (no audio upload, no extra
compute), so it costs the same 1 quota unit, charged only on a served result.
The differentiator vs opaque taggers (e.g. Cyanite) is that EVERY tag carries its own
`confidence` and `provenance`:
- confidence: measured (our Essentia analysis) | derived (MIREX mood from valence+energy) |
model-estimated (AcousticBrainz mood SVM probability — research-grade, raw prob in `value`) |
catalog-genre (broad catalogue tag, not fine-grained).
- provenance: essentia | valence+energy | acousticbrainz | catalog.
`value` is the [0,1] score for numeric tags and null for label-only tags (mood category, genre).
Provide AT LEAST ONE identifier: `track` (optionally with `artist`), `isrc`, `mbid`,
`spotify_id`, or `track_id` (catalog itunes_track_id). If you know several, send them all —
they resolve by precedence (`track` > `isrc` > `track_id` > `mbid` > `spotify_id`) and the
rest are ignored, so you never have to pick. The broad, reliable coverage is the
MEASURED tags from our Essentia analysis over the analysed catalogue (plus on-demand by name);
MBID/ISRC additionally reach 7.5M+ AcousticBrainz recordings WHEN you supply that identifier.
Returns { track, count, tags:[{tag, category, value, confidence, provenance}], disclaimer }.
For the full numeric feature set use get_audio_features; for nearest tracks use a discovery tool.Get a compact, HONESTLY-LABELLED tag list for a track — energy / danceability / valence / acousticness / instrumentalness, plus a mood tag and a broad genre tag. It is a tag-shaped projection of the same open-data analysis get_audio_features returns (no audio upload, no extra compute), so it costs the same 1 quota unit, charged only on a served result. The differentiator vs opaque taggers (e.g. Cyanite) is that EVERY tag carries its own `confidence` and `provenance`: - confidence: measured (our Essentia analysis) | derived (MIREX mood from valence+energy) | model-estimated (AcousticBrainz mood SVM probability — research-grade, raw prob in `value`) | catalog-genre (broad catalogue tag, not fine-grained). - provenance: essentia | valence+energy | acousticbrainz | catalog. `value` is the [0,1] score for numeric tags and null for label-only tags (mood category, genre). Provide AT LEAST ONE identifier: `track` (optionally with `artist`), `isrc`, `mbid`, `spotify_id`, or `track_id` (catalog itunes_track_id). If you know several, send them all — they resolve by precedence (`track` > `isrc` > `track_id` > `mbid` > `spotify_id`) and the rest are ignored, so you never have to pick. The broad, reliable coverage is the MEASURED tags from our Essentia analysis over the analysed catalogue (plus on-demand by name); MBID/ISRC additionally reach 7.5M+ AcousticBrainz recordings WHEN you supply that identifier. Returns { track, count, tags:[{tag, category, value, confidence, provenance}], disclaimer }. For the full numeric feature set use get_audio_features; for nearest tracks use a discovery tool.
Für dieses Tool wurde kein Eingabeschema veröffentlicht.
12 von 12 Tools haben eine Beschreibung veröffentlicht.
Tool-Namen und -Beschreibungen stammen vom Publisher und werden wortgetreu als inerter Text angezeigt. Es sind die Zeichenketten, die ein MCP-Client an ein Modell übergibt, deshalb prüft Forge sie auf Prompt-Injection-Muster — jeder Befund erscheint oben beim Sicherheits-Scan. „Privilegiert“ ist ein Schlagwort-Treffer im Tool-Namen, keine Prüfung dessen, was das Tool tut: ein harmlos klingender Name kann trotzdem alles tun.
Audio features + harmonic set-building for tracks by name/ISRC. Spotify audio-features replacement.
Verlinkte Namen öffnen den Forge-Index aller Einträge, bei denen dieses Tool beobachtet wurde. Alle indexierten Tools durchsuchen.
Dieser Eintrag veröffentlicht kein npm-Paket, daher hat Forge keinen Abhängigkeitsbaum dafür. Das ist eine Lücke in der Abdeckung — keine Aussage, dass er keine Abhängigkeiten hat.