I built an MCP that helps LLM's interpret Audio.

I built a local MCP server that gives LLMs ears — real audio analysis, not transcription. Point an LLM at any audio file on your machine and it can tell you the key, tempo, dynamics, stereo field, structural sections, and how everything evolves over time. Pure Rust DSP, no Python, no FFmpeg, runs locally.

The trick is token efficiency. Full analysis of a 3-minute track uses under 1% of context. Tools encourage a birds-eye-first workflow: get the structural map, then zoom into sections that matter at high resolution.

Best use case so far: mixing/mastering feedback for bedroom producers. An LLM can tell you your low-mids are muddy, your stereo field collapses to mono in the chorus, and you're 2dB hot for Spotify.

Sample Conversation (missing sections are visualisation artifacts Claude created)

Sample Visualisation Output built by Claude from analysis data (Bohemian Rhapsody)

[GitHub link]