AcoustID MCP for AI. Identify any song using its audio fingerprint.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
AcoustID identifies songs using audio fingerprints—it's basically Shazam for developers. You feed it a clip, and it returns full metadata, like the artist, album, and MusicBrainz ID.
Need to search recordings by name or find all associated fingerprints? This MCP handles it, giving you deep access to music library data without needing an API key for basic lookups.
What your AI can do
Get recording metadata
Gets full details for a recording—like artists and release groups—using an existing AcoustID.
Lookup by fingerprint
Identifies a song by taking an audio fingerprint string and the clip's duration.
Search by mbid
Finds all associated fingerprints for a specific MusicBrainz ID, useful if you know the recording but not the fingerprints.
Submit an audio fingerprint, and the MCP returns matching recordings with confidence scores, titles, and artists.
Retrieve all associated details—like the original artist, album name, and release groups—using a known AcoustID.
Look up records using general search terms like an artist's name or a song title.
Use a MusicBrainz ID to retrieve every associated audio fingerprint, which is useful for batch processing.
Submit new audio fingerprints to expand the database with information about unknown songs.
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AcoustID: 5 Tools for Audio Metadata Search
These five tools let you interact with the entire AcoustID database, allowing you to search recordings by name, find fingerprints from an ID, or submit new audio data.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using AcoustID on VinkiusGet Recording Metadata
Gets full details for a recording—like artists and release groups—using an existing AcoustID.
Lookup By Fingerprint
Identifies a song by taking an audio fingerprint string and the clip's duration.
Search By Mbid
Finds all associated fingerprints for a specific MusicBrainz ID, useful if you know...
Search By Recording
Locates matching recordings by providing general text like an artist or song name.
Submit Fingerprint
Adds a new audio fingerprint to the system, requiring the chromaprint string and...
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Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Start with AcoustID, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 5 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Tracing an Unknown Song's Provenance
Right now, finding out what song was playing at a party or in a movie scene means opening multiple web tabs: Google Search for the title, then Shazam to check fingerprints, and maybe another site just to verify the album art. It's manual work that involves copy-pasting IDs from one service into another.
With this MCP, you feed your agent the audio clip once. Your AI client handles all the cross-referencing—it runs the fingerprint match, retrieves metadata via `get_recording_metadata`, and gives you a complete dossier on the track without ever leaving the chat window.
AcoustID MCP: Getting Full Music Records
The process of finding all related data used to take multiple steps: first, running a basic search by name, then taking the resulting IDs and manually feeding those into separate lookup tools. It was slow and required keeping track of five different identifiers.
Now, you can use `search_by_mbid` or `get_recording_metadata` to pull all associated details in one go. The data flow is clean: you give the ID, you get the complete record. That’s it.
What your AI can actually do with this
This connector lets your agent identify songs just by listening to a short audio clip. You don't need to know the title or artist; you just submit a fingerprint and get immediate results. It pulls rich metadata, linking the song directly to its MusicBrainz ID and providing details about the release group.
The system also allows you to search for recordings using general criteria, like an artist name, or look up all associated fingerprints if you already have a known MusicBrainz ID. If you're building anything audio-related, connecting this MCP through Vinkius means your AI client has immediate access to thousands of music records and metadata types.
Plus, you can even submit new fingerprints if you find a song that needs adding to the database.
019d8412-3efc-723b-94d8-d4dc5a993ef1 Here's how it actually works
The bottom line is that you get actionable, standardized music data without having to manage complex authentication keys.
Start by connecting your AI client to this MCP on Vinkius. No API key is needed for basic lookups.
You provide the necessary data—whether it's an audio fingerprint, a MusicBrainz ID, or just a title and artist name.
The MCP processes the request against the AcoustID database and returns structured metadata about the matching recordings.
Who is this actually for?
This MCP is for audio engineers and researchers who can't rely on simple web searches. If your job requires identifying unknown background tracks or cataloging massive amounts of media, you need this.
Integrating instant song identification into an application so users never have to guess what they're listening to.
Analyzing large datasets of audio fingerprints and MusicBrainz IDs to map out relationships between recordings and artists.
Building backend services that need a reliable, programmatic way to verify media assets or enrich user-submitted content with metadata.
What Changes When You Connect
Instant identification: Use lookup_by_fingerprint to instantly match an unknown track by analyzing a short audio clip, giving you titles and artists right away.
Deep metadata access: Pull comprehensive details for any record via get_recording_metadata, including associated release groups and original recording metadata.
Cross-reference data: If you have a MusicBrainz ID, use search_by_mbid to find every related audio fingerprint—a huge time saver for researchers.
Broad searching capability: When you only know the artist or title, search_by_recording finds the necessary identifiers without requiring specific fingerprints.
Database contribution: Use submit_fingerprint to expand your knowledge base by adding newly captured songs directly into the AcoustID database.
See it in action
Tracking down a background track
An audio engineer captures 10 seconds of ambient music. Instead of uploading it to three different web services, they ask their agent to run the fingerprint through lookup_by_fingerprint. The result immediately provides the artist and album name for inclusion in the project credits.
Validating a media asset batch
A researcher acquires 50 audio files, all supposedly from the same forgotten band. Instead of manually searching each one, they use search_by_mbid on known identifiers to quickly verify if all fingerprints link back to the correct source material.
Building a media catalog
A developer is building an audio player feature and needs to know the metadata for every track. They use search_by_recording with known artist names, then feed those results into get_recording_metadata to build a complete, structured database.
Expanding the song library
A music enthusiast records an unindexed local recording. They use the system's capability to submit a fingerprint, allowing them to contribute the new data via submit_fingerprint and build out the resource for future users.
The honest tradeoffs
Searching only by title
Trying to search using only a song title, which often returns too many irrelevant results or fails because it lacks context.
If you know the artist and title, use search_by_recording. If you have an audio clip, always prioritize lookup_by_fingerprint for the most accurate match.
Assuming a single identifier type
Getting frustrated because the system only accepts a fingerprint string and refuses to accept the raw MP3 file.
You must use lookup_by_fingerprint with the actual chromaprint fingerprint string and audio duration (seconds), not the original file.
Ignoring known IDs
Starting a search from scratch when you actually possess the MusicBrainz ID for the track.
Always start with search_by_mbid. It's the most direct way to pull every related piece of metadata and fingerprint without needing audio input.
When It Fits, When It Doesn't
Use this MCP if your core problem revolves around identifying songs or retrieving rich, structured data based on audio clips or known music identifiers (like MusicBrainz IDs). It excels when you have a partial match—a fragment of an ID, a few seconds of sound. Don't use it if you simply need to read a text file or write basic code; those are better handled by general-purpose agent tasks. If your data is structured and already indexed (e.g., just names), focus on search_by_recording. But if the unknown variable is the audio itself, lookup_by_fingerprint is non-negotiable.
Questions you might have
How do I identify a song using AcoustID MCP? +
You run lookup_by_fingerprint by providing the chromaprint fingerprint string and the audio duration in seconds. The system returns matching records, titles, and artists.
What is the best way to search for a song using AcoustID MCP? +
If you only know the name or artist, use search_by_recording. This tool handles general text inputs and finds relevant records across the database.
Can I get full metadata using AcoustID MCP? +
Yes. Use get_recording_metadata with an existing AcoustID to retrieve comprehensive details, including artist names and release group information.
How do I add a song fingerprint using AcoustID MCP? +
You use the submit_fingerprint tool. You must provide the chromaprint fingerprint string and audio duration; you can optionally include name and artist for better indexing.
Does search_by_mbid work if I don't know the fingerprints? +
Yes, that is exactly what it does. If you have a MusicBrainz ID but no specific fingerprint, search_by_mbid retrieves all associated audio fingerprints for you.
If I know a MusicBrainz ID but not the fingerprints, how do I use `search_by_mbid`? +
It finds all associated AcoustID fingerprints for that single MusicBrainz recording. This is useful because it lets you gather every available fingerprint linked to the known song record.
When I run `search_by_recording`, what specific IDs do I receive for a found track? +
You get the matching recording's AcoustID, MusicBrainz ID, title, and artist name. This initial search gives you key identifiers needed to perform more detailed metadata lookups.
Does `lookup_by_fingerprint` require both the fingerprint string and audio duration? +
Yes, it requires both parameters to run successfully. You must provide the Chromaprint fingerprint and the precise audio length in seconds for a match attempt.
Do I need an API key? +
No! AcoustID's public API works without authentication using a default client key for basic lookups.
How does audio fingerprinting work? +
AcoustID uses Chromaprint (fpcalc) to generate a fingerprint from audio. This fingerprint is matched against the database to identify the song. You need to run fpcalc on an audio file to get the fingerprint string.
What is MusicBrainz? +
MusicBrainz is an open music encyclopedia that collects music metadata. AcoustID links audio fingerprints to MusicBrainz recordings, enabling song identification through the MusicBrainz ID (MBID).
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