"MCP Bybit Server Repository on GitHub" matching MCP servers:

Local spoken notifications for Codex with TTS and always-on-top overlay.

Enables spoken conversations with Claude on a Mac: Claude speaks through speakers, listens to the user's natural replies, and transcribes them locally. No audio leaves the computer, and it includes tools for voice setup and a hands-free voice mode.

Image, video, chat and text-to-speech models (GPT Image, Gemini Image, Veo, Kling, Seedance, Claude, GPT, Gemini) behind one API key. generate_image returns a preview the model can see, and review_image has a vision model critique the result and propose a corrected prompt, so an assistant can generate, check and fix images on its own.

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 coding agents to synthesize and play concise spoken summaries of their work through multiple TTS providers, including Azure, OpenRouter, OpenAI, Gemini, ElevenLabs, and local OpenAI-compatible endpoints. It also lets agents stop playback on demand and check which provider, voice, and audio bridge are currently active.

Enables spoken consulting case interview practice through Claude, providing real-time cases, grading on structure, analytics, judgment, and communication, and targeted practice drills.

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 AI apps to transcribe local audio and video files on the user's own GPU, producing SRT/VTT subtitles and text without cloud uploads. It also helps find media files, list available devices, and list supported languages and models.

A simple MCP server that can send notifications on mac devices.

Enables text-to-speech functionality on macOS using the say command, offering extensive control over speech parameters like voice, rate, volume, and pitch for a customizable auditory experience.

Provides VOICEVOX text-to-speech as an MCP tool. Requires a running VOICEVOX engine on localhost.

Integrates Textwell with the Model Context Protocol for facilitating text operations such as writing and appending text through a GitHub Pages bridge.

Enables agents to speak text and read documents aloud on the user's computer, with tools for speaking, reading, stopping playback, and saving audio.

An Alexa+ MCP add-on that turns a parent's description of their child's day into an original illustrated, narrated bedtime story with word-by-word read-along highlighting, branching choices, a word of the night, and a persistent story world that carries characters and past episodes from one night to the next. It runs the writing, painting and narration pipeline in the background on Amazon Bedrock and Polly while keeping every tool call under Alexa+'s latency budget, with Bedrock Guardrails checking the parent's request and every page.

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.

Enables agents to control a transparent, always-on-top desktop character window over a loopback-only local HTTP API — applying expressions, motions, parameters and gaze, speaking via TTS or uploaded audio with lip-sync, capturing screenshots, and switching models, window placement and visibility. Everything stays local, so no cloud account or manual token handling is required.

Exposes a single transcribe tool over streamable HTTP so containerised agents can send a media file name and receive text transcribed locally by MacWhisper on the host Mac's GPU, with token-gated access and no uploads or API keys. Callers place media in a configured directory, and the blocking call returns the finished transcript.

Enables AI agents to give spoken output by synthesizing text into speech with an on-device neural model and playing it through the user's speakers, entirely offline. Agents can queue spoken summaries and stop playback on demand.

Exposes a local, hands-free voice room to any MCP client, letting agents read speaker-labelled conversation turns, wait for the next turn, list who is present, and speak aloud through local text-to-speech. Speech recognition, speaker identification and voice output all run on the user's own machine, so agents receive text with speaker names rather than audio.

Enables coding agents to hold spoken conversations by exposing speech-to-text and text-to-speech tools over MCP, routing them to a local daemon that drives a Chrome window using on-device speech recognition and Piper synthesis. It supports listening, speaking, and reading files aloud within a Claude Code plugin or an MCP gateway.