"Categorizing messages such as emails or system logs" matching MCP servers:

Plays built-in Windows system sounds or WAV files to notify users when tasks finish or need attention, using the winsound module. Provides a single play_sound tool with presets and customizable file paths, repeats, and intervals.

Enables detection of AI-generated content in images, videos, audio, and text via the AI or Not API. Supports media analysis tools for deepfakes, synthetic voices, and AI-written text.

MCP server for audio transcription using OpenRouter models, supporting verbatim, cleaned, and custom transcription modes.

Enables remote control of audio playback and system volume on a Raspberry Pi via the Model Context Protocol (MCP).

An MCP server that enables voice-to-voice AI conversations using ElevenLabs for speech synthesis and recognition, with tools for voice management, text-to-speech, and speech-to-text.

MCP server that provides a transcribe_audio tool to convert voice messages from channels into text using OpenAI Whisper, enabling Claude Code to process audio attachments.

Enables MCP clients to measure a track's loudness and peak levels, strip artifacts left by AI generators, compare mastering presets, and master single tracks or batches to streaming targets via a hosted remote server or a local stdio bridge. Tracks are supplied as public HTTPS links, with analysis, cleanup and previews free and full masters limited by a daily free quota or credits.

Enables AI agents to turn YouTube videos into downloadable audio files in formats like MP3, M4A, WAV, FLAC, and OGG (with optional clipping), and to retrieve existing captions as text, JSON, Markdown, SRT, or VTT. Uses your own RapidAPI key and supports job polling for longer conversions.

Generates room impulse responses as WAV files from natural language descriptions, enabling agents to create realistic or arbitrary reverb for audio production.

Captures and transcribes system audio in real-time using OpenAI Whisper, enabling meeting transcription, content creation, and accessibility through natural language.

Render, analyze, and verify audio (WAV or FLAC) through a fully offline, deterministic engine, exposed as MCP tools for AI agents.

An MCP server that enables transcribing local audio files and Telegram voice messages using OpenAI's Whisper via local inference or cloud API. It supports multiple audio formats, automatic language detection, and optional word-level timestamps for AI-powered audio analysis.

Enables MCP clients such as Claude Code, Cursor, and VS Code to generate speech through three MiMo TTS models: nine built-in voices (with singing and style/audio tags), voices designed purely from a text description, and voices cloned from a local mp3/wav sample. Audio is streamed and written only inside a sandboxed output directory, with non-overwriting filenames.

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 AI agents to perform local audio tasks such as speech synthesis, voice cloning, music and sound effect generation, and audio editing through MCP, with GPU models loaded on demand and released after idle.

Enables AI agents to analyze local or URL-hosted audio files with TrackTag, returning professional music metadata such as BPM, key, genres, moods, and 35+ fields. Runs locally on your machine, using your TrackTag API key and credit balance for analyses.

Lets MCP clients such as Claude Code, Claude Desktop and Cursor generate background music, instrument loops and sound effects locally on Windows from plain-language prompts. It also edits and processes the results — variations, inpainting, extension, trimming, loudness normalization, seamless looping and WAV/OGG/FLAC export for game engines and video editors.

Enables generating game assets — images, 3D meshes, sound and music — through free Hugging Face Spaces with no GPU or subscription, plus procedural synthesizers that consume no quota at all. Adds licence gating, per-take provenance metadata, and post-processing such as audio trimming/looping/normalising and six-angle Blender mesh renders.

A job queue-based FFmpeg wrapper enabling AI assistants to perform video processing tasks such as trimming, format conversion, resolution change, and subtitle conversion through natural language.

Enables local analysis of PCM WAV files to produce bounded audio observations such as activity segments, clipping indicators, duration, sample rate, peak, and RMS level without uploading recordings.