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Alternatives to FlowSpeech MCP Server

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
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      maintenance
      A Model Context Protocol server that integrates high-quality text-to-speech capabilities with Claude Desktop and other MCP-compatible clients, supporting multiple voice options and audio formats.
      11 npm
      1
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    • A
      license
      Not graded
      quality
      A
      maintenance
      A text-to-speech MCP server with 48 voices across 9 languages, supporting emotion spans, SFX tags, and multi-speaker dialogue. Deployable via a single npx command with built-in guardrails and swappable backends.
      20 PyPI
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    • A
      license
      B
      quality
      D
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      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.
      2
      171 npm
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    • A
      license
      B
      quality
      F
      maintenance
      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
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    • A
      license
      A
      quality
      D
      maintenance
      A Model Context Protocol server that enables AI models to generate and play high-quality text-to-speech audio through your device's native audio system using Rime's voice synthesis API.
      1
      202 npm
      27
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    TDQS

    A3.6/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool has a clearly distinct role: single-speaker TTS, two-speaker TTS, voice catalog, quota, job lookup, and local request-ID generation. The main potential overlap between tts and tts_multi is resolved by descriptions specifying single- versus two-speaker output.

    Naming Consistency4/5

    All tools share the flowspeech_ prefix and lowercase snake_case, making them predictable. The only minor inconsistency is that some names are verb-led (list_voices, get_job) while others are noun/acronym-led (tts, quota, new_request_id).

    Tool Count5/5

    Six tools is well-scoped for a focused TTS API surface. Each tool appears to serve a distinct necessary purpose without obvious redundancy.

    Completeness4/5

    The surface covers the core workflow: request-ID creation, single- and multi-speaker synthesis, voice discovery, quota checking, and job status lookup. Minor gaps may exist around job listing/cancellation or explicit audio retrieval, but the essential lifecycle is represented.

    Maintenance

    ActivityMaintained
    ResponsivenessSyncing