elevenlabs-mcp
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- FlicenseNot gradedqualityCmaintenanceAn MCP server that provides text-to-speech, speech-to-text, and voice management via ElevenLabs API.1-
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- AlicenseNot gradedqualityDmaintenanceOfficial MCP server that enables interaction with ElevenLabs Text to Speech and audio processing APIs. It allows generating speech, cloning voices, transcribing audio, and creating sound effects through natural language.MIT

ElevenLabs MCP Serverofficial
AlicenseBqualityFmaintenanceAn official Model Context Protocol (MCP) server that enables AI clients to interact with ElevenLabs' Text to Speech and audio processing APIs, allowing for speech generation, voice cloning, audio transcription, and other audio-related tasks.271,539MIT- FlicenseAqualityBmaintenanceAn agent-first MCP server for generating and transforming audio (music, speech, sound effects) via ElevenLabs and Mureka, providing tools for transcription, voice cloning, and multi-speaker dialogue.33-
- AlicenseAqualityCmaintenanceMCP server that brings ElevenLabs to Claude Code — text-to-speech, sound effects, music generation, voice cloning, speech-to-speech, transcription, and voice isolation. 8 tools for industry-leading AI audio.8MIT
TDQS
Scored across 28 tools
Each tool has a clearly distinct purpose: text generation, voice cloning, dubbing, transcription, sound effects, music composition, etc. No significant overlap; even similar-sounding tools like text_to_speech, text_to_dialogue, and speech_to_speech are well-differentiated by their descriptions.
All tools use consistent snake_case naming with a verb_noun pattern (e.g., list_voices, delete_history_item, download_dubbed_audio). Minor deviations like forced_alignment are still in the same style, and there is no mixing of conventions like camelCase.
28 tools is on the higher side but appropriate given the breadth of ElevenLabs features (TTS, voice management, dubbing, sound effects, music, etc.). Each major capability has its own set of tools, so the count feels comprehensive rather than bloated.
The tool surface covers most key workflows: voice CRUD, generation (TTS, STS, sound effects, music), dubbing (start, status, download), transcription, alignment, and usage stats. Minor gaps exist (e.g., no list_dubbing_jobs tool, no rename_voice), but agents can work around them.