"Information about modal concepts or elements" matching MCP servers:

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.

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.

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.

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.

Enables AI clients to transcribe YouTube links and uploaded audio or video files, then retrieve the resulting transcripts as text, SRT, VTT, or JSON with timestamps and optional speaker labels. Also supports paging through long transcripts, checking upload progress, listing or deleting jobs, and viewing remaining account minutes.

Enables real-time fusion of 2D/3D visual bounding boxes, head-pose gaze vectors, and acoustic beamforming azimuth angles into cross-modal focal salience scores to resolve deictic spatial references for multimodal agents.

Enables AI agents to fuse 2D/3D visual bounding boxes, user head-pose gaze vectors, and acoustic beamforming azimuth angles into cross-modal focal salience scores. It also resolves deictic spatial references in real time, running as a zero-dependency Python MCP server over JSON-RPC stdio.

Exposes the program's push-to-talk transcript history to MCP clients such as Claude Desktop and Claude Code, letting them fetch recent or latest transcripts, query results by time window, range, or ISO timestamp, search text, look up records by id, and read counts, runtime status, or clear history. Each result carries millisecond-precision hold start/end timestamps alongside the transcribed text.

Enables MCP clients to speak text aloud on macOS using the built-in say command, with no external speech API or keys, and to list available voices and control rate or voice selection. Requests from multiple client threads share a single FIFO playback worker, so speech plays one item at a time and can be inspected, stopped, or muted in hold or discard mode.

Transcribes YouTube videos or audio files to Markdown, plain-text, and Word documents.

Enables AI agents to submit and manage durable long-form audio transcription jobs, and read or export structured transcripts via MCP tools.

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

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.

MCP server + web dashboard for TouchDesigner. Inspect, optimize, and control TD patches from Claude Code or the browser.

Enables MCP-capable agents to control a running LMMS session to compose, arrange, mix, and export music, including creating tracks, editing notes, loading presets and samples, automating parameters, and rendering audio or MIDI.

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 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.