$ loading_
Inspect local audio files with playback, metadata, loudness, and spectrogram analysis.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "Audio File MCP App" MCP server from askskill: Run: claude mcp add 'io-github-counterpoint-studio-audio-file-mcp-app' -- npx -y @counterpoint-studio/audio-file-mcp-app
Please inspect the local audio file ./audio/podcast.wav and return a playable preview, file metadata, and key audio parameters.
Returns playback results, a metadata summary, and basic details such as format and duration.
Analyze the loudness of ./audio/ad-spot.mp3, describe the overall loudness, and flag whether it may be too loud or too quiet.
Provides loudness analysis to help determine whether level adjustment is needed.
Generate a spectrogram for ./audio/interview.m4a and summarize observable characteristics from the spectrum.
Returns a spectrogram or spectrum result with a brief description of the frequency distribution.
Developers, designers, or researchers can use it to quickly review playback, metadata, loudness, and spectrogram information for local audio files without switching between multiple tools.
When you need to confirm basic file properties, this tool helps inspect local audio metadata and key audio details for organization or troubleshooting.
When evaluating whether audio needs further processing, you can use it to review loudness behavior and spectrogram characteristics as an initial analysis step.
It is used to inspect local audio files, including playback, metadata viewing, loudness analysis, and spectrogram-related inspection.
Based on the name and description, it is intended for local audio files. Support for remote resources is not stated in the provided information.
The provided material does not include installation steps, runtime, or dependency requirements. See the source repository for details.
Enable AI to transcribe audio, detect languages, and extract metadata.
Inspect and safely manage Audiobookshelf libraries through MCP workflows.
Transcribe audio into structured notes with speakers, timestamps, summaries, and action items.
Transcribe audio, split recordings intelligently, and analyze meetings in MCP clients.
Master audio with loudness analysis, mixing, LUFS targeting, and Dolby export.
Analyze audio structure, rhythm, and key with structured JSON and visual outputs.