"Understanding the Concept of LLM Context" matching MCP servers:

Enables local video inspection and offline transcription with timestamped JSON and Markdown output, enforced filesystem boundaries, and reproducible content-addressed caching.

Enables coding agents to watch and analyze videos by extracting scene-aware frames, transcribing speech, and generating shot timelines, all constrained by a token budget to fit LLM context limits.

Enables Claude Code to leverage Chinese LLMs for multimodal tasks including image analysis, audio transcription, and deep synthesis via MCP protocol.

Enables Alexa+ to retain and recall a lightweight per-user context thread across separate voice interactions, supporting starting, querying, and listing active contexts for continuity.

Enables LLM agents to process local videos into timestamped, citable text documents and then query them through tools for listing videos, retrieving transcripts, and fetching specific segments, all fully offline.

Enables AI assistants to summarize, take notes on, and answer questions about YouTube, Bilibili, and Xiaohongshu videos by providing subtitles and local speech transcription with timestamps via MCP.

Local MCP server for private, consent-based speaker identification and attribution, with per-language voiceprints and calibrated match thresholds. It lets AI assistants know who is speaking in a room or transcript without cloud services.

Give your AI assistant eyes and ears — analyze any video, audio, or image, entirely on your machine.

Enables AI assistants to download YouTube videos and transcribe them locally using OpenAI's Whisper speech recognition model.

A FastMCP server that exposes a locally-hosted llama.cpp LLM as MCP tools, plus utilities for weather, news, web fetching, file I/O, and more. Enables MCP-compatible clients to use a local GGUF model for text generation without external APIs.

A self-hosted MCP server that connects AI clients to local media-generation backends like ComfyUI and Blender, and provides built-in utilities for video/audio analysis, editing, subtitles, speech, and scene detection via a persistent file cache.

MCP server for local speech-to-text using Whisper Large V3 (MLX), enabling audio transcription with text/timestamps/SRT output and LLM-based correction, all running offline on Apple Silicon.

Enables MCP clients such as Claude Code, Codex and Cursor to retrieve timestamped transcripts of public YouTube videos, either from cache, from native captions, or via asynchronous audio-generation jobs that are polled by job ID. It returns the source URL, selected language and millisecond-offset segments without summarizing the video itself.

Extract structured knowledge from voice recordings. Transcribes audio using Mistral's Voxtral model and lets your LLM agent handle post-processing within your existing setup.

Provides browser automation, audio transcription, and LLM chat as MCP tools for any agent.

Enables AI agents to transcribe audio, detect ASR hallucinations deterministically, repair flagged segments with guarded LLM calls, score transcript quality, and estimate pipeline costs over stdio.

Enables real-time transcription and heuristic vocal stress analysis of live financial webcasts, providing an LLM with rolling transcripts and a stress score.

Enables AI agents to send and verify signup OTPs, place expected outbound calls, and fetch call transcripts through the Model Context Protocol.

MCP server that converts video links into AI-generated Markdown notes, with tools for task management, transcription engines, and LLM providers.

An MCP server for Djelia that brings Bambara transcription, translation, and text-to-speech to any LLM.