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mcp-audio-analysis

by hugohow
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Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
loadB

Loads an audio file and returns the path to the audio time series Offset and duration are optional, in seconds. Be careful, you will never know the name of the song.

get_durationC

Returns the total duration (in seconds) of the given audio time series.

tempoC

Estimates the tempo (in BPM) of the given audio time series using librosa. Offset and duration are optional, in seconds.

chroma_cqtA
Computes the chroma CQT of the given audio time series using librosa.
The chroma CQT is a representation of the audio signal in terms of its
chromatic content, which is useful for music analysis.
The chroma CQT is computed using the following parameters:
- path_audio_time_series_y: The path to the audio time series (CSV file).
    It's sometimes better to take harmonics only
- hop_length: The number of samples between frames.
- fmin: The minimum frequency of the chroma feature.
- n_chroma: The number of chroma bins (default is 12).
- n_octaves: The number of octaves to include in the chroma feature.
The chroma CQT is saved to a CSV file with the following columns:
- note: The note name (C, C#, D, etc.).
- time: The time position of the note in seconds.
- amplitude: The amplitude of the note at that time.
The path to the CSV file is returned.
mfccC
Computes the MFCC of the given audio time series using librosa.
The MFCC is a representation of the audio signal in terms of its
spectral content, which is useful for music analysis.
The MFCC is computed using the following parameters:
- path_audio_time_series_y: The path to the audio time series (CSV file).
    It's sometimes better to take harmonics only
beat_trackB
Computes the beat track of the given audio time series using librosa.
The beat track is a representation of the audio signal in terms of its
rhythmic content, which is useful for music analysis.
The beat track is computed using the following parameters:
- hop_length: The number of samples between frames.
- start_bpm: The initial estimate of the tempo (in BPM).
- tightness: The tightness of the beat tracking (default is 100).
- units: The units of the beat track (default is "frames"). It can be frames, samples, time.
download_from_urlC

Downloads a file from a given URL and returns the path to the downloaded file. Be careful, you will never know the name of the song.

download_from_youtubeC

Downloads a file from a given youtube URL and returns the path to the downloaded file. Be careful, you will never know the name of the song.

Prompts

Interactive templates invoked by user choice

NameDescription
analyze_audio Creates a prompt for audio analysis. Feel free to customize the text below to explain how users can interact with the tools.

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 8 tools

Disambiguation4/5

Most tools have distinct purposes in audio analysis (e.g., beat tracking vs. chroma vs. MFCC), but download_from_url and download_from_youtube overlap significantly in function—both download audio files from different sources with similar descriptions. The core analysis tools are well-differentiated, but the download tools could cause confusion.

Naming Consistency3/5

The naming is mixed: some tools use descriptive verb_noun patterns (beat_track, get_duration), while others are single verbs (load, tempo) or noun phrases (chroma_cqt, mfcc). There's no consistent convention across all tools, though most names are readable and hint at their function.

Tool Count4/5

With 8 tools, the count is reasonable for an audio analysis server, covering key operations like loading, downloading, and various analysis features. It's slightly lean but not incomplete, as it includes essential functions without being overwhelming.

Completeness3/5

The server covers basic audio analysis tasks (loading, downloading, duration, tempo, spectral features), but there are notable gaps: no tools for editing, filtering, or exporting audio, and no way to visualize results. The analysis tools are focused on computation but lack broader workflow support.

Resources