Reaper MCP Server

Overview

Reaper MCP Server bridges the gap between a DAW (Digital Audio Workstation) and AI assistants by exposing Reaper project data to Claude Desktop through the Model Context Protocol. The server solves a common pain point for audio engineers and developers: querying intricate project details—such as track layouts, FX chains, routing matrices, or metadata—without manually parsing the proprietary Reaper project format. By providing a programmatic interface to these projects, developers can automate documentation, perform quality checks, or generate creative prompts that reflect the current state of a session.

At its core, the server offers two lightweight tools. scans a user‑specified directory and lists all files, while converts a chosen project into a structured JSON object. When an AI assistant receives a question about a particular session, it first locates the file with and then feeds the parsed JSON to its reasoning engine. This two‑step workflow ensures that Claude always works with up‑to‑date, machine‑readable data, enabling precise answers such as “Which tracks have a reverb plugin?” or “How many instances of the chorus effect are used across the mix?”

Key capabilities include:

• Project discovery: Quickly locate all Reaper sessions in a given workspace, facilitating bulk operations or batch analysis.
• Deep parsing: Expose every element of a project—tracks, items, envelopes, FX chains, routing—to the AI in a consistent JSON schema.
• Tool integration: Seamlessly appear as native tools within Claude Desktop, allowing users to invoke them via natural language or the hammer icon.
• Data transparency: Developers can inspect the raw JSON output, ensuring that the AI’s responses are grounded in actual project content.

Typical use cases span from automated workflow audits—such as detecting unused tracks or mismatched channel counts—to creative assistance, where the assistant can suggest arrangement changes based on the current track hierarchy. In educational settings, students can ask for explanations of complex routing schemes or learn how specific effects are applied across a session. The server’s lightweight design means it can be embedded in larger audio production pipelines, where AI assistants act as on‑call helpers for mixing engineers or sound designers.

By exposing Reaper’s internal data to AI through MCP, developers gain a powerful tool for enhancing productivity, reducing manual oversight, and unlocking new possibilities in audio creation and analysis.