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Voxtral TTS MCP Server

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Voxtral TTS MCP Server

Voxtral TTS Online Free Text to Speech & Voice Clone Speech

MCP Badge Node License: MIT Read Only Zero Config MCP

A Model Context Protocol server that exposes the canonical Voxtral TTS knowledge surface — voice and TTS workflows, FAQ, official links — to MCP-compatible AI clients such as Claude Desktop, Cursor, Windsurf, and Continue. Read-only, no API keys, no quota, ~50 ms cold start.

Official website: https://voxtraltts.online

🎙️ About Voxtral TTS

Voxtral TTS is Mistral AI's text-to-speech platform, accessible at voxtraltts.online, that converts written text into realistic, emotionally expressive speech across nine languages. Built on a 3.4-billion-parameter transformer decoder backbone paired with a flow-matching acoustic transformer and a neural audio codec, the system is designed for production environments where voice quality, low latency, and speaker consistency across languages all matter. The service is available as a hosted API at $0.016 per 1,000 characters, and the underlying model weights are also published on Hugging Face for teams that prefer self-hosted deployment. Whether you need a single narrator voice that holds up across French and Arabic, or a real-time voice agent that responds in under 100 milliseconds, Voxtral TTS is built to cover that range.

Related MCP server: leanvox-mcp

Key Features

  • Low-latency inference: The model delivers approximately 70 ms latency with a real-time factor of around 9.7x, making it suitable for interactive voice applications and live agents rather than just batch narration jobs.

  • Zero-shot voice cloning: A 3-to-25-second reference audio clip is enough to adapt the model to a custom speaker identity, with no fine-tuning pipeline required.

  • Cross-lingual speaker consistency: The same cloned voice can be reused across all nine supported languages — English, French, German, Spanish, Dutch, Portuguese, Italian, Hindi, and Arabic — without losing the speaker's characteristic sound.

  • Emotional style control: Outputs can be guided toward distinct expressive styles including neutral, happy, and sarcastic, giving teams control over tone without recording separate voice assets.

  • Built-in voice library: A set of ready-to-use named voices (Margaret, Paul, Marie, Oliver, and others) lets teams get started immediately without providing reference audio.

  • Flexible deployment: The API integrates directly through Mistral Studio and standard REST endpoints; the open-source weights option allows air-gapped or on-premise deployments for compliance-sensitive environments.

Use Cases

  • Customer support automation: Generate voice responses for IVR systems, automated routing menus, and support bots that need to sound consistent across long sessions and multiple languages.

  • Product onboarding and explainer narration: Turn documentation, tooltips, or walkthrough scripts into spoken audio without recording studio sessions, and update the audio as fast as you update the text.

  • Multilingual marketing and localization: Produce regional campaign audio from a single script using the same speaker voice across language variants, keeping brand voice coherent in every market.

  • Real-time voice agents: Power conversational AI agents, virtual assistants, or phone bots where the round-trip from text to audible speech needs to stay well under a second.

  • Regulated-industry workflows: Use the self-hosted model weights to run speech synthesis entirely within a private infrastructure, meeting data residency requirements in financial services, healthcare, or manufacturing contexts.

Who Is It For

Voxtral TTS is aimed at product teams, engineers, and growth operators who are building voice features into applications rather than looking for a one-off audio tool. The API-first design and per-character pricing model suit developers who want to integrate TTS into a larger pipeline — whether that is a customer-facing chatbot, a localization workflow, or an internal voice agent. The zero-shot cloning capability and cross-lingual consistency make it especially useful for teams serving multilingual audiences who cannot afford to maintain separate voice recordings per language. Teams in regulated industries benefit from the open-source weight option, which lets them run inference entirely on their own infrastructure.

Tools

list_voices

Return the canonical voice and TTS configuration exposed on the site. (Voxtral TTS)

Input: no parameters. Returns: text/markdown.

get_official_links

Return the canonical list of official links for Voxtral TTS (website, support, docs when available).

Input: no parameters. Returns: text/markdown.

Resources

  • site://voxtraltts/voices — Supported voices, languages, and TTS modes.

  • site://voxtraltts/faq — Short FAQ generated from public site metadata.

  • site://voxtraltts/links — Canonical URLs to share with users.

Prompts

tell_me_about_voxtraltts

Summarize what the site is, who it's for, and how it works. — Voxtral TTS

read_aloud_demo_voxtraltts

Plan a read-aloud workflow with the site's voices. — Voxtral TTS

Installation

Install via Smithery

npx -y @smithery/cli install voxtraltts-mcp --client claude

(Replace claude with cursor, windsurf, or continue for those clients.)

Install from source

git clone https://github.com/rocnubie/voxtraltts-mcp.git
cd voxtraltts-mcp
pnpm install

Then add to your MCP client config (claude_desktop_config.json for Claude Desktop, mcp.json for Cursor / Windsurf / Continue):

{
  "mcpServers": {
    "voxtraltts-mcp": {
      "command": "node",
      "args": [
        "/absolute/path/to/voxtraltts-mcp/src/index.mjs"
      ]
    }
  }
}

Debug with MCP Inspector

npx @modelcontextprotocol/inspector node src/index.mjs

Official Links

Development

pnpm install
pnpm start                 # run the server over stdio

License

MIT

Available Tools

2 tools
list_voicesA

Return the canonical voice and TTS configuration exposed on the site. (Voxtral TTS)

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the behavioral burden. It mentions the return content but does not disclose side effects, read-only nature, or authentication requirements. For a simple list operation, this is minimally acceptable but not fully transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence with a parenthetical tag. Every word adds value, and it is front-loaded with the core purpose. No extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the tool is simple (0 params, no output schema), the description does not elaborate on the return structure or format. It is complete enough for basic use but could benefit from specifying what a 'canonical voice' entails.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters and the input schema coverage is 100% (empty object). The baseline for 0 params is 4, and the description adds no additional parameter context, which is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns 'the canonical voice and TTS configuration exposed on the site', specifying the resource and origin. It distinguishes from the sibling 'get_official_links' which serves a different purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The description implies it is the tool for obtaining voice/TTS info, but lacks when-not-to-use or exclusion criteria, making it adequate but unremarkable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv0.1.0
    • First observedget_official_links
    • First observedlist_voices
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TDQS

A3.8/5.0
Disambiguation5/5

The two tools have entirely different purposes: one returns voice and TTS configuration, the other returns official links. No ambiguity.

Naming Consistency5/5

Both tools follow the verb_noun pattern (list_voices, get_official_links), providing consistent and predictable naming.

Tool Count2/5

Only 2 tools for a TTS server is too few; core functionality like text-to-speech synthesis is missing, making the set feel incomplete.

Completeness1/5

The server claims to be a TTS MCP but lacks any actual speech synthesis tool. Essential operations for the domain are absent.

Resources

Unclaimed servers have limited discoverability.

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