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MCPGeneration-for-ElevenLabs

MCPGeneration-for-ElevenLabs

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    • A
      license
      B
      quality
      F
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      An 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.
      27
      1,544
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      A local MCP server that turns any text into narration, voice-overs, dialogues, audiobook chapters and per-shot video clips as MP3/WAV files — with emotions, acting modes, inline cues, presets and subtitles — driven by a ChatGPT sign-in rather than an API key. It also exposes tools for inspecting audio (timings, pace, pitch, waveform), transcribing recordings, listing and favouring voices, saving pronunciation presets, and checking sign-in status.
      MIT

    TDQS

    C2.7/5.0

    Scored across 89 tools

    Disambiguation3/5

    The server has many intentionally parallel tools, especially non-streaming vs streaming vs timestamped variants for speech, dialogue, and music, plus several usage/budget/history read tools. Descriptions usually clarify boundaries, but the sheer number of near-duplicate generation paths makes misselection likely for an agent.

    Naming Consistency5/5

    Tool names consistently use snake_case verb_noun patterns such as generate_speech, list_voices, get_asset, delete_asset, and update_voice_settings. Minor suffixes like _stream and _with_timestamps are predictable and coherent.

    Tool Count1/5

    Eighty-nine tools is an extreme mismatch for a single MCP server, even one covering a broad media API. It far exceeds the practical 3-15 range and creates a large selection burden, duplicate pathways, and cognitive overhead.

    Completeness4/5

    The surface covers a very wide domain: TTS, dialogue, sound effects, music, dubbing, voices, history, assets, image/video generation, flows, and local FFmpeg operations. There are only minor lifecycle gaps, such as limited update operations for some resources, but no major dead ends.