How do I use speaches-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@speaches-mcp transcribe the audio at /tmp/recording.mp3" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

speaches-mcp

by xavier-hernandez

Overview Schema Related Servers Score Discussions

JavaScript

Remote

speaches-mcp

An MCP (Model Context Protocol) server that exposes speaches as transcribe_audio and text_to_speech tools.

Speaches is a local, OpenAI API-compatible server for speech-to-text (via faster-whisper) and text-to-speech (via Kokoro/Piper). This MCP server lets AI assistants like Claude use it directly.

Tools

`transcribe_audio`

Transcribe an audio file using your speaches instance.

Parameter	Required	Description
`file_path`	✅	Absolute path to the audio file
`language`	❌	ISO-639-1 language code (e.g. `en`). Omit for auto-detect.
`model`	❌	Whisper model ID. Defaults to `SPEACHES_STT_MODEL` env var.

`text_to_speech`

Convert text to speech and save to a file.

Parameter	Required	Description
`text`	✅	Text to convert
`output_path`	✅	Absolute path for the output audio file (e.g. `/tmp/output.mp3`)
`voice`	❌	Voice ID. Defaults to `TTS_VOICE` env var.
`model`	❌	TTS model ID. Defaults to `TTS_MODEL` env var.

Usage

With Docker + Supergateway (SSE transport)

This exposes the MCP server over SSE on port 8010, suitable for remote clients.

docker compose up --build

Then connect your MCP client to http://localhost:8010/sse.

Standalone (stdio transport)

Build the image:

docker build -t speaches-mcp .

Run it:

docker run --rm -i \
  -e SPEACHES_URL=http://your-speaches-host:8000 \
  speaches-mcp

For Claude Desktop, add to your config:

{
  "mcpServers": {
    "speaches": {
      "command": "docker",
      "args": ["run", "--rm", "-i",
        "-e", "SPEACHES_URL=http://your-speaches-host:8000",
        "speaches-mcp"
      ]
    }
  }
}

Environment Variables

Variable	Default	Description
`SPEACHES_URL`	`http://speaches:8000`	Base URL of your speaches instance
`STT_MODEL`	`Systran/faster-whisper-large-v3`	Default speech-to-text model
`TTS_MODEL`	`speaches-ai/Kokoro-82M-v1.0-ONNX`	Default text-to-speech model
`TTS_VOICE`	`af_heart`	Default TTS voice
`OPENAI_API_KEY`	`dummy`	Required by the OpenAI SDK but not used by speaches

Downloading Models

Before transcribing, make sure you've downloaded models into speaches:

# Speech-to-text
curl http://your-speaches-host:8000/v1/models/Systran/faster-whisper-large-v3 -X POST

# Text-to-speech
curl http://your-speaches-host:8000/v1/models/speaches-ai/Kokoro-82M-v1.0-ONNX -X POST

License

MIT

Available Tools

2 tools

text_to_speechD

ParametersJSON Schema

Name	Required	Description
`text`	Yes	Text to convert to speech
`output_path`	Yes	Absolute path to write the output audio file (e.g. /tmp/output.mp3)
`voice`	No	Voice ID to use. Defaults to af_heart
`model`	No	TTS model ID to use. Defaults to speaches-ai/Kokoro-82M-v1.0-ONNX

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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

Completeness1/5

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

Tool has no description.

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

Parameters1/5

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

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

transcribe_audioD

ParametersJSON Schema

Name	Required	Description
`file_path`	Yes	Absolute path to the audio file to transcribe
`language`	No	ISO-639-1 language code, e.g. 'en', 'fr'. Omit for auto-detect.
`model`	No	Whisper model ID to use. Defaults to Systran/faster-whisper-large-v3

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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

Completeness1/5

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

Tool has no description.

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

Parameters1/5

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

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

C2/5.0

Disambiguation5/5

The two tools have clearly distinct purposes: text_to_speech converts text to audio, and transcribe_audio converts audio to text. There is no overlap.

Naming Consistency5/5

Both tool names follow a consistent verb_noun pattern using underscores, making them predictable and easy to understand.

Tool Count3/5

With only two tools, the server feels thin for a speech processing domain, where additional tools for voice management or language selection might be expected.

Completeness4/5

The core operations of text-to-speech and transcription are covered, but missing features like voice listing or parameter configuration create minor gaps.

Resources

GitHub Repository

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