Melrose MCP Server

Overview

The melrose‑mcp server bridges the gap between AI assistants and music production by exposing a lightweight Model Context Protocol interface to the melrōse music‑programming tool. Developers can now let language models compose, modify, and play melodies directly from their AI workflows without leaving the conversational interface. This eliminates the need for manual code generation, syntax errors, and external tooling, enabling rapid prototyping of musical ideas.

At its core, the server offers a small but powerful set of tools that map directly to common music‑related actions:

• melrose_play – sends a melrōse expression to generate and output sound.
• melrose_bpm – adjusts the tempo of the current session.
• melrose_devices and melrose_change_output_device – list available MIDI outputs and switch the active receiver.
  These commands are intentionally simple, allowing a language model to construct JSON payloads that the server translates into real‑time MIDI events sent to a DAW or hardware synthesizer.

Developers benefit from the server’s tight integration with existing AI assistants such as Claude Desktop. By adding a single configuration entry that points to the binary, the assistant can invoke any of the melrōse tools as if they were native extensions. This means a user can request “play the first bar of Für Beethoven” or “slow down the tempo to 80 BPM,” and the assistant will translate that into a melrōse expression, adjust settings, and stream audio back to the user—all without leaving the chat.

Real‑world use cases include interactive music education, where an AI tutor can demonstrate scales or chord progressions; live performance support, allowing a performer to cue loops and tempo changes on the fly; and rapid composition for podcasts or video games, where a writer can ask an LLM to generate thematic material in the style of Debussy or Mike Oldfield. Because melrōse uses a concise domain‑specific language, the server can also expose higher‑level abstractions (e.g., “play a C# chord”) that are easy for LLMs to understand and manipulate.

What sets melrose‑mcp apart is its focus on real‑time audio output through standard MIDI. Unlike many text‑to‑music systems that produce static files, this server streams live sound to any connected synthesizer or DAW. Combined with the low‑overhead MCP interface, developers can build responsive music generation pipelines that integrate seamlessly into existing AI assistant ecosystems.