"An open-source penetration testing framework" matching MCP servers:

Enables secure, self-hosted audio transcription through a single MCP interface, letting users route recordings from authorized sources to Microsoft MAI, ElevenLabs Scribe, or Groq Whisper and receive a consistent transcript schema.

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.

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

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.

Analyze an hour-long YouTube video sentence by sentence in three minutes, and extract the information you need from the screen. Open-source local MCP server for AI clients.

MCP server that turns any audio or video source into structured, searchable intelligence for agents, enabling download, transcription, semantic search, speaker identification, and more.

An MCP (Model Context Protocol) server that provides seamless integration between Fish Audio's Text-to-Speech API and LLMs like Claude, enabling natural language-driven speech synthesis.

Lets AI agents search, read and export call recordings, transcripts and AI meeting notes from Digisensus Recorder, a free open-source call recorder for macOS, and start or stop recording. Runs locally on the Mac and talks to the app over a Unix socket.

Enables agents to list, read, export, and derive text from local VOIVOX transcript sessions without modifying the immutable source.

Enables ChatGPT on the web to generate and play spoken audio through an embedded player, exposing a speak tool that renders text with selectable styles and scenes via configurable text-to-speech providers. It routes each request across ElevenLabs, Cartesia, Azure Speech, and DashScope in a configured failover order, picking providers by language and scenario.

Enables chat-driven audio analysis and enhancement using local Claude, including denoising, EQ, compression, and loudness normalization, with an A/B viewer for synchronized comparison.

Gives an AI assistant its own real phone number and email address so it can place and answer calls, book reservations by phone, send and receive texts, and read written summaries, structured details, and transcripts of every conversation. Also manages contacts, persona and voice settings, webhooks, and a unified call/message history through a hosted OAuth-secured MCP endpoint requiring no local code.

An MCP server enabling video processing via natural language: transcription with Whisper, segment cutting with FFmpeg, and file management.

Enables an AI agent to read and send real iMessages—including text, voice notes, stickers, links, images, and reactions—through a managed cloud phone line without needing a Mac.

Enables users to manage Gmail accounts using AI agent-assisted operations via an MCP protocol, supporting email search, reading, deletion, and sending with a voice-powered interface.

MCP server for fetching YouTube video transcripts without an API key.

Turns a single XState-compatible finite state machine config into a voice-driven MCP surface, generating one tool per event and a resource exposing the current state, context, and available actions. Lets voice assistants and other MCP clients drive stateful conversations and workflows from one source of truth, with guards enforced at call time and state prompts used as spoken replies.

An MCP server for AI voice synthesis with an inline audio player, allowing users to give their AI assistant a custom cloned voice using DashScope or ElevenLabs.

Enables AI assistants to synthesize speech using a custom cloned voice through MiniMax TTS and present it in an inline audio player with waveform visualization and transcript controls.

Enables an assistant to turn any video ffmpeg can read into a text transcript by extracting the audio, splitting it on silence, and transcribing each piece on-device through utt. Exposes tools to start a transcription job with optional hints and timestamps, and to poll for a transcript that is still being worked on.