live-translate-mcp
The live-translate-mcp server enables real-time English ↔ Mandarin speech translation, using on-device speech-to-text (Whisper) and text-to-speech (Piper), with Claude handling the linguistic translation. ASR and TTS run fully locally; only the translated text is sent to the Anthropic API.
translate_file: Pass an absolute path to a WAV file. The server auto-detects the language, transcribes the speech, translates it via Claude, synthesizes the translated audio with Piper TTS, saves the output as<name>_translated.wavnext to the original, plays it back, and returns the original text, translation, and output file path.translate_speech: Send base64-encoded WAV audio directly and receive the transcription, translation, and synthesized translated audio as base64 WAV — ideal for programmatic workflows.health_check: Verify that all required dependencies (Whisper model cache, Piper TTS voice files, andespeak-ng) are present and ready before making translation requests.Automatic language detection: No need to specify the source language — English or Mandarin Chinese is detected automatically from the audio.
live-translate-mcp
Real-time English ↔ Mandarin speech translation for Claude — powered by Whisper, Claude AI, and Piper TTS.
Give Claude the ability to listen, translate, and speak. live-translate-mcp is a Model Context Protocol (MCP) server that adds speech translation as a native tool inside Claude Desktop and Claude Code. Hand it an audio file, and it transcribes, translates, synthesises, and plays the result — entirely on your machine, with Claude handling the translation.
What it does
Step | Technology | Where it runs |
Speech → Text | OpenAI Whisper (via | Local |
Text → Translation | Claude (Opus 4.8) | Anthropic API |
Translation → Speech | Piper TTS (ONNX) | Local |
Audio never leaves your machine except for the translated text sent to the Claude API. ASR and TTS run fully on-device.
Related MCP server: Voice MCP
Tools
translate_file
Translate a WAV audio file. Pass an absolute path — the server transcribes it, translates the text via Claude, synthesises speech, saves <name>_translated.wav next to the original, and plays it automatically.
Translate /Users/alice/meeting_clip.wavReturns: original text, translation, and the path to the saved output file.
translate_speech
Translate raw audio passed as a base64-encoded WAV string. Returns the transcription, translation, and synthesised audio as base64 WAV — useful for programmatic workflows.
health_check
Verify that all dependencies (Whisper model cache, Piper voice files, espeak-ng) are present and ready before making a translation request.
Installation
No installation required. Run it directly with npx:
npx -y live-translate-mcpOr install globally:
npm install -g live-translate-mcpPrerequisites:
Node.js 18+
An Anthropic API key
espeak-ng— for TTS phonemisation (brew install espeak-ngon macOS,apt install espeak-ngon Linux)
The Whisper model (~150 MB) and Piper voice models (~200 MB) download automatically on first use and are cached in ~/.live-translate/.
Claude Desktop setup
Add the following to your Claude Desktop configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"live-translate": {
"command": "npx",
"args": ["-y", "live-translate-mcp"],
"env": {
"ANTHROPIC_API_KEY": "your-api-key-here"
}
}
}
}Restart Claude Desktop. You'll see live-translate appear in the tools panel.
Claude Code setup
claude mcp add live-translate -- npx -y live-translate-mcpThen set your API key in the environment before starting Claude Code, or pass it via the MCP env config.
Usage examples
Once configured, just ask Claude naturally:
"Translate this audio file for me: /Users/alice/recording.wav"
"Use translate_file on /tmp/interview.wav"
"Check if live-translate is ready"
Claude will call the appropriate tool automatically.
Supported languages
Language | ASR | Translation | TTS |
English | ✓ | ✓ | ✓ |
Mandarin Chinese (普通话) | ✓ | ✓ | ✓ |
Language is detected automatically from the audio — no need to specify it.
How it compares
live-translate-mcp | Cloud speech APIs | |
ASR | Local (Whisper) | Remote |
Translation | Claude API | Remote |
TTS | Local (Piper) | Remote |
Audio privacy | Audio stays on device | Audio uploaded |
Cost | Claude API only | Per-minute pricing |
Offline | Partially (ASR + TTS) | No |
Environment variables
Variable | Default | Description |
| — | Required. Your Anthropic API key. |
|
| Path to Piper |
Related
live-translate — the standalone CLI for push-to-talk translation with no API key required, using local models end-to-end.
Model Context Protocol — the open standard this server implements.
Piper TTS — the local TTS engine powering speech synthesis.
Licence
MIT
Available Tools
3 toolshealth_checkA
Check whether all live-translate-mcp dependencies are available: Whisper model cache, Piper voice files, and espeak-ng binary.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description lists the dependencies checked but does not detail what happens if a dependency is missing (e.g., error vs. warning), nor the return format. No annotations provided to supplement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with purpose, no wasted words. Efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple zero-parameter tool, the description covers what is checked. Could mention output but not essential given simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so the description adds no parameter info. Baseline score of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks availability of three specific dependencies (Whisper, Piper, espeak-ng), distinguishing it from translation tools like translate_file and translate_speech.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 vs alternatives; usage is implied as a prerequisite check for translation operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
translate_fileA
Translate a speech audio file between English and Mandarin Chinese. Pass an absolute path to a WAV file. Automatically detects the input language and translates to the other. Saves the translated audio next to the source file and plays it. Returns original text and translation.
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | Absolute path to a WAV audio file (16 kHz, mono, 16-bit recommended) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description fully discloses behavior: auto-detects language, saves audio next to source, plays it, and returns text. No mentions of side effects like file overwrite, but overall transparency is good.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Five sentences, front-loaded with purpose, but somewhat repetitive ('automatically detects...', 'saves and plays'). Could be tightened without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers input format, language pair, output actions, and return value. Missing details like error handling or file size limits, but sufficient given tool simplicity and no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema already covers 'file_path' with recommendations (16 kHz, mono, 16-bit). Description adds minimal new info ('absolute path') beyond repeating schema content. Schema coverage is 100%, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool translates speech audio between English and Mandarin Chinese using a WAV file. It specifies the input and output, and implicitly distinguishes from sibling 'translate_speech' by focusing on file translation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance. It implies usage for translating speech audio files but doesn't compare to alternatives like 'translate_speech' or discuss prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
translate_speechB
Translate speech audio between English and Mandarin Chinese. Automatically detects the input language and translates to the other. Returns original text, translation, and synthesised audio as base64-encoded WAV.
| Name | Required | Description | Default |
|---|---|---|---|
| audio_base64 | Yes | Base64-encoded WAV audio (16 kHz, mono, 16-bit recommended) | |
| sample_rate | No | Sample rate of the input audio in Hz (default: 16000) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains the core behavior: auto-language detection, bidirectional translation, and output components. However, it does not disclose potential side effects, required permissions, rate limits, or error handling. As there are no annotations, more safety context would be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, using two well-structured sentences. The first sentence states the action and language pair; the second adds output details. No redundant or unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description sufficiently covers the tool's purpose, input format, and output structure (original text, translation, base64 audio). However, it does not specify that the translation is limited to English-Mandarin only, nor does it differentiate from the sibling translate_file, which may be relevant for similar use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides detailed descriptions for both parameters (audio_base64 format and sample rate defaults). The tool description does not add new information about the parameters beyond what the schema offers, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool translates speech between English and Mandarin Chinese, with automatic language detection and output of original text, translation, and synthesized audio. However, it does not distinguish itself from the sibling tool 'translate_file', which could potentially translate speech files or text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus its siblings (health_check, translate_file) or when not to use it. It does not mention any prerequisites, limitations, or preferred scenarios.
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.
3 tool updates
v0.1.7- First observed
health_check - First observed
translate_file - First observed
translate_speech
TDQS
Each tool has a clearly distinct purpose: health_check checks dependencies, translate_file handles file-based translation with local saving, and translate_speech provides real-time speech translation with base64 output. No overlap.
All tool names use consistent snake_case naming with a verb_noun pattern (health_check, translate_file, translate_speech), making it easy to infer functionality.
With 3 tools, the server is small but well-scoped for its purpose of live translation between English and Mandarin. The count is reasonable, though slightly on the lower end, covering health check, file translation, and speech translation.
The server covers the core workflow of speech translation and file conversion but lacks text translation, language configuration, or support for additional language pairs, which are notable gaps for a translation tool.
Maintenance
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