"Java command reference and usage" matching MCP servers:

Enables AI assistants in MCP clients to turn long videos from YouTube, VK Video, RuTube, Dzen, Twitch, Yandex Disk, Google Drive or direct links into captioned vertical Shorts/Reels, transcripts and articles, returning links to the finished clips. It also exposes read-only modes and job quoting so clients can inspect sources and estimate credit usage before creating anything.

Enables AI agents to generate spoken audio locally on a GPU via the Chatterbox models, with zero-shot voice cloning from reference clips, multilingual output, and sentence-aligned chunking for long text.

Enables an AI assistant to place user-approved phone calls in the callee's language through your own Bland or Twilio account, with language detection, script generation, cost estimates, and safety guard rails. Includes a preview-then-confirm gate, plus call status, transcripts, and summaries.

Fast MCP Telegram — Production-grade Telegram MCP server with direct MTProto API, multi-user ACL, dual transport (stdio + HTTP SSE), voice transcription, and context-optimized design. One-command setup with uvx fast-mcp-telegram

Local-first CLI + MCP server for meeting recordings - Whisper transcription,

Unofficial MCP server for Memobot, enabling access to user profile, recordings, transcripts, summaries, and account usage through reverse-engineered API endpoints.

Enables AI agents to manage voice agents, place calls, run campaigns, and monitor usage and billing via the Wixzel Phone API.

Enables MCP clients to submit completed speech or typed text to a self-hosted decision engine and receive judgments such as ordinary speech, command, retained thought, uncertainty, or no action, along with session and decision inspection.

MCP server for text-to-speech using macOS say command, enabling speech synthesis, audio file generation, and voice management.

Enables text-to-speech synthesis using Coqui TTS with multiple models and languages, long-text chunking, customizable speed and speaker settings, and voice cloning from reference audio.

A Model Context Protocol server that provides voice-AI session management and usage tracking tools for SpekoAI. It enables creating, retrieving, and ending voice sessions while monitoring usage through MCP clients like Claude Desktop.

Screen-aware voice command layer for AI agents that captures screen context and instructions to build agent-ready commands for various tools.

Transcribe any video URL to text with one command, supporting 1000+ sites via yt-dlp and multiple ASR providers.

Enables local, offline, CPU-only transcription of video/audio into timestamped SRT subtitles with Whisper, Chinese TTS dubbing (including multi-speaker dialogue), and synthesis of a new dubbed video in a single command. Supports dubbing silent videos, translating and re-voicing foreign-language videos, and batch-adding narration to short clips within AI workflows.

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 macOS users to hear short spoken summaries of agent replies using the built-in say command, while keeping full details in the chat. Works with any MCP-compatible host and requires no cloud or API key.

Enables natural language generation of speech in any language, voice creation and management, music composition, dubbing, pronunciation dictionaries, and account history/usage through any MCP client using your own API key.

Enables natural voice interaction with Claude Code through speech-to-text, supporting wake word activation and multiple backends like Whisper and Google. It allows users to execute commands and control their coding environment hands-free via their microphone.

Enables running and managing Claude Code from Telegram with two-way voice messages, persistent memory across restarts, scheduled messages, calendar access, and live tool-call visibility through Agentic Mode. It also provides a self-healing daemon and an Agents Board for monitoring sessions, subagents, and token usage.

Feedback ingestion for AI agents. Record your screen and talk; your agent does the rest — files the bugs, writes the spec, builds the backlog.