"Understanding the text content of a webpage" 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 AI agents to transcribe Instagram Reels into text by providing a tool that accepts a Reel URL and returns the speech transcript along with metadata such as author and duration.

Text-to-speech MCP server that enables AI assistants to read text aloud on the user's computer using Windows SAPI, with no API key or cloud service required.

Enables users to look up a Douyin creator by name, public ID, or profile URL and receive the latest video link, like/comment/favorite/share counts, a full timestamped transcript with an AI-reviewed correction and change log, and a content overview. Collection, media download, and local speech transcription are orchestrated as stdio MCP tools so Codex, WorkBuddy, and other compatible clients can run creator research and scheduled checks without extra API keys.

Enables downloading videos from platforms like YouTube and converting them to text using OpenAI Whisper and ffmpeg. It supports multiple output formats including TXT, JSON, SRT, and VTT for transcriptions.

Enables converting text to speech audio in 20+ languages, returning base64-encoded MP3 output via Google TTS with x402 micropayment-based pay-per-call access.

An MCP server that provides speech-to-text transcription and speaker diarization using OpenAI Whisper and pyannote.audio.

An MCP server for content creation that extracts video transcripts, scrapes web articles, and performs web searches.

Self-host a realtime voice-call companion for coding agents. Exposes an MCP endpoint that agents can poll as an alternate input stream.

Enables users to convert text into high-quality audio by accessing the OpenAI Text-to-Speech API. It supports customizable model selection and voice options for synthesized speech generation via the MCP protocol.

Enables text-to-speech conversion using Google Translate's TTS, supporting many languages and accents, and saving MP3 files.

Enables voice assistants to find nearby service providers, connect users to live video representatives for natural conversation, and handle appointment booking. It also returns confirmed bookings so the assistant can add them to its calendar.

Enables agents to convert text to speech using OpenAI's TTS models with voice selection, delivery instructions, and queue-based audio playback. Supports both blocking and non-blocking modes for flexible audio generation and playback control.

An MCP server that downloads videos/extracts audio from various platforms like YouTube, Bilibili, and TikTok, then transcribes them to text using OpenAI's Whisper model.

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

Enables MCP clients such as Claude to generate spoken audio locally, narrating English with Kokoro and Malayalam with IndicF5, Parler-TTS, Piper or MMS, including cloned Malayalam voices, pronunciation lexicons, and number/currency expansion. Outputs loudness-normalised WAV or MP3 and runs long narrations through a pollable job queue so tool calls never time out.

Enables submitting audio via URL or Base64 for local Whisper transcription, then checking job status and results or deleting transcription jobs. It provides asynchronous job handling and SSRF-protected downloads/callbacks without cloud API calls.

Enables MCP-capable chat clients to synthesize speech from text, convert it with a local RVC voice model, and render the result in an inline audio player.

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.

Enables hair colourists to capture a client's colour formula by voice, retrieve it on the next visit, and scale the mix to today's bowl, with per-client memory and clarification when names are ambiguous.

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 continuous voice conversation with AI coding assistants by locally transcribing speech with Whisper and delivering utterances as text prompts.

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.

Lets AI assistants understand what you're working on — current screen content, recent dictation, clipboard, and saved notes — running entirely on your own machine with nothing sent to the cloud.

Enables text-to-speech and speech-to-text through MCP tools, using Groq's free hosted endpoints when configured and falling back to fully local keyless models otherwise. Also provides voice listing and provider health checks.

Voice interface for Claude Code: you talk, the agent listens, codes, and talks back while it works. Live speech-to-text with turn-taking, Grok/xAI voices with per-subagent personas, and a real-time HUD dashboard.

Enables agents to inspect local videos, audio recordings, and documents by producing storyboard grids, timestamped transcripts, page images, and page-aligned text, with tools for probing media, zooming into details, locating terms, and reading subtitles. It works only with local absolute paths and does not download content or call an LLM.

An MCP server that makes AI agents speak a brief summary of every response out loud using TTS.

Enables progress-triggered text-to-speech in Cursor Agent, with speak and stop controls and voice matching the system UI language.

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.