"Search for information about 'daw' (Digital Audio Workstation)" matching MCP servers:

Enables headless offline audio processing through MCP tools for rendering stems, analyzing audio and masking, auto-mixing, applying EQ/compression, mastering, comparing references, and running release checks. It works on local audio files without a DAW window, audio interface, GPU, model download, or cloud account.

Enables AI agents to build songs by editing a JSON song model: searching a licence-tagged sample library, adding step-pattern and time-stretched loop tracks, setting gain/pan, describing songs, and rendering them to 44.1 kHz WAV/MP3 with release-safety warnings. Songs and renders are read from and written to a project root, so an agent can go from empty project to finished track through MCP tool calls.

Audio perception for AI agents. Measure loudness, clipping, noise and pitch; view spectrograms; split audio into events; transcribe speech; compare before/after; and clean up, edit or synthesize sound. Runs locally, no API keys.

A mock-first MCP server for audio generation that creates placeholder WAV files locally, enabling development and testing of audio workflows without APIs or costs.

An interactive digital audio workstation as an MCP server, enabling music production with a channel rack, piano roll, mixer, effects, automation, microphone recording, and offline WAV rendering.

Local MCP server for macOS that captures microphone input and/or system audio output into explicit, user-defined sessions and exposes the raw WAV audio to AI agents through MCP tools.

Enables Claude AI to generate professional, production-ready MIDI files (chord progressions, drum patterns, bass lines, melodies, and full arrangements) compatible with any DAW including Logic Pro, Ableton Live, FL Studio, and more.

Enables AI-powered music production by integrating with REAPER DAW via MCP protocol, allowing users to control project settings, MIDI editing, and VST plugins through natural language.

An MCP server that masters audio with a professional signal chain using ffmpeg, enabling loudness analysis, vocal-over-beat mixing, full mastering to target LUFS, and Dolby codec export.

Enables comprehensive audio file analysis and metadata extraction with specialized game audio development features, supporting batch processing of multiple formats and providing platform-specific optimization recommendations.

Enables an agent to inspect an audio folder and report what is inside each file — duration, sample rate, channels, codec and bitrate — and to group byte-identical files, identical decoded audio stored in different containers, and near-duplicate candidates, with suggested keeps and explicit differences, all read-only.

Enables batch audio processing and optimization using FFmpeg with preset configurations for game audio, voice processing, and music mastering, including specialized optimization for ElevenLabs AI voice output.

Enables AI-powered game audio creation for Unreal Engine 5.7, integrating Wwise, MetaSounds, and Blueprint through MCP tools to generate DSP graphs, sound systems, and trigger logic.

Enables audio file analysis using Google's Gemini multimodal models with support for additional context and system instructions to guide the model's behavior.

Enables local-first, cross-platform audio calibration workflows combining REW-guided measurement, conservative EQ analysis, DSP export/apply adapters, and measured post-change verification.

Enables text-to-speech conversion with smart language detection (using Google TTS for Chinese) and audio file playback with playback controls for Claude Desktop.

Enables local, CPU-only audio post-production through tools for denoising, LUFS loudness normalization, silence removal, format conversion, pitch shifting, and speed changes. Supports podcast and video platform loudness compliance workflows.

A portable, Dockerized Python tool that implements Model Context Protocol for audio transcription using Whisper models, featuring both CLI and web UI interfaces for converting audio files to JSON transcriptions.

Enables agents to conjure individual sounds by semantic text search over a CLAP-embedded audio database and render them into stereo, 5.1, or 7.1.4 spatial mixes, with tools for summing mixes, analyzing a mix's sounds and spatial balance, and mimicking existing mixes. Documentation resources are exposed so the client can read usage guides on demand.

Enables AI models to analyze audio files through numerical fingerprints, pitch tracking, and visual spectrograms without requiring direct audio playback. It provides tools for comparing audio iterations and detecting patterns using token-efficient analysis operations.

Enables playback control of local audio files through a virtual audio output device, supporting play, stop, and status queries with configurable root directory and path safety enforcement.

Provides powerful video and audio editing capabilities through FFmpeg, enabling AI assistants to perform professional-grade operations including format conversion, trimming, overlays, transitions, and advanced audio processing.

Enables playing and inspecting local audio files in an MCP host with an in-conversation UI showing waveform, spectrogram, and loudness metrics, while exposing playback state and metadata to the model.

AI-powered music production toolkit enabling AI agents to generate, remix, separate stems, master tracks, and control DAWs via MCP.

Enables AI agents to perform local video, audio, and file operations inside an isolated workspace, including cutting/concat videos, extracting audio, transcribing, and managing files, with typed responses and background job support.

MCP server that bridges Ableton Live with AI models, enabling real-time project inspection and control such as track overview, device parameters, and audio analysis.

ismail is a DAW your agent drives through MCP, CLI or Python. The agent writes notes, instruments, effects and automation as data. ismail's own engine renders the audio, and every render comes back as text the agent can read. No AI model generates the audio. It runs on your own machine. It is free and open source. https://github.com/newsbubbles/ismail

Enables AI agents to search, watch, summarize, clip, and extract transcripts from YouTube videos, all without needing an API key or leaving the chat.

Enables searching, retrieving metadata, and downloading YouTube videos or audio without requiring an API key. It utilizes yt-dlp to support media retrieval and playlist management within MCP-compliant clients like Claude and Cursor.

Enables AI assistants to inspect and organize Adobe Premiere Pro projects, edit timelines with frame-accurate cuts and trims, animate motion and opacity keyframes, and trigger exports through natural language.