"Information about Docker" matching MCP servers:

Enables controlling a Dirtywave M8 over USB by reading its screen as text, pressing buttons, playing notes, and retrieving system information through MCP tools.

Enables asynchronous Microsoft Edge text-to-speech synthesis over MCP stdio, producing MP3 or OGG/Opus audio with optional SRT/WebVTT subtitles and word-timing JSON, plus Docker deployment support.

An MCP server for OBS Studio that subscribes to events and maintains a history, enabling queries about audio levels, source states, and past actions rather than only one-shot requests.

An MCP server that enables AI assistants to search, analyze, and retrieve information about audio samples from Freesound.org through their API.

A powerful MCP tool for parsing and manipulating MIDI files that allows users to read, analyze, and modify MIDI files through natural language commands, supporting operations like reading file information, modifying tracks, adding notes, and setting tempo.

An MCP server for parsing and analyzing Ableton Live .als project files offline, enabling queries about tempo, tracks, clips, and MIDI data through tools like als_summary and als_list_tracks.

Enables searching and downloading audio samples from Freesound using keywords, filters, and sound IDs. It provides detailed sound metadata including duration, license information, and preview URLs.

Gives an agent a full video editing room: import footage, cut and order shots, add captions and audio, composite, replace screens via SAM2, render, and export in multiple formats, all locally in Docker with no API key required.

A Model Context Protocol server that integrates with VOICEVOX engine to provide text-to-speech synthesis and speaker information retrieval, allowing users to generate and play voice audio from text.

A Model Context Protocol server that lets your AI download YouTube audio and search videos, powered by yt-dlp and ffmpeg, shipped as a Docker image.

Enables AI agents to enroll and verify voice samples against known identities, providing honest confidence signals about voice authenticity.

Enables transcription of audio files using Google Gemini AI and creates a searchable knowledge base, allowing users to ask natural language questions about content in meetings, podcasts, lectures, and other audio recordings.

Enables agents to access OpenAI-compatible transcription, live ASR, text-to-speech, voice cloning, phoneme recognition, and server-side file staging through streamable HTTP MCP tools.

Enables AI agents to perform comprehensive audio processing—stem separation, analysis, transcription, restoration, speech processing, and generation—through a unified self-hosted API with asynchronous jobs and webhooks.

Enables local inspection of Apple MainStage state and patch/set information, plus MIDI control such as program changes, CC, and metronome toggle via MCP.

Gives coding agents the ability to hear and reason about audio by describing sounds, matching them to text, locating key moments, detecting realness, generating timelines, and critiquing soundtracks. It also lets agents synthesize and iteratively improve their own sounds in code.

Connects AI coding agents to PreSonus StudioLive III mixers over the local network, exposing live mixer context such as channel names, mute/solo/fader state, Fat Channel compressor/EQ models, and meter activity as MCP resources and tools for reading and reasoning about live sound engineering.

Exposes Spotify search, playlist, library, discovery, and playback tools to AI assistants over MCP Streamable HTTP. Supports Spotify OAuth, multi-client MCP OAuth with PKCE and dynamic client registration, encrypted token storage, and self-hosted Docker deployment.

Integrates with Freesound.org to enable searching, discovering, and previewing audio content such as sound effects and music loops. It provides detailed metadata and licensing information to support video editing and content creation workflows.

A Docker-containerized MCP proxy that provides AI image generation, text generation, vision analysis, and text-to-speech capabilities through REST endpoints using Pollinations AI services. Enables multimodal AI interactions including image creation, transformation, OCR, and audio generation through standard HTTP APIs.

Provides voice recognition and text extraction capabilities with support for both stdio and MCP modes, processing audio files or base64 encoded data and returning structured results with language, emotion, and speaker information.

Enables asking questions about image, audio, or video files using state-of-the-art multimodal models. Powered by fal.ai for advanced media analysis and understanding capabilities.

Enables text-only AI agents to ask questions about images, audio, and video by passing file paths or URLs to a multimodal model and returning text answers.

Enables acoustic diagnosis of household appliance faults via real-time FFT spectral analysis, staging matching OEM replacement parts, while enforcing a capability-based information-flow firewall that blocks lethal hazard instructions and prompt-injection attacks at the MCP transport boundary.

Enables coding agents to ask focused questions about local screenshots, video, audio, and music and receive compact text reports on OCR, layout, timestamps, sounds, and music description. Media stays on the machine and raw files are never loaded into the agent's context, with support for cropping, interval sampling, and durable background jobs.