Live Translate MCP vs Xcomet MCP Server

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_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.

xcomet_evaluate

Evaluate the quality of a translation using xCOMET model. This tool analyzes a source text and its translation, providing: - A quality score between 0 and 1 (higher is better) - Detected error spans with severity levels (minor/major/critical) - A human-readable quality summary Args: - source (string): Original source text to translate from - translation (string): Translated text to evaluate - reference (string, optional): Reference translation for comparison - source_lang (string, optional): Source language code (ISO 639-1) - target_lang (string, optional): Target language code (ISO 639-1) - response_format ('json' | 'markdown'): Output format (default: 'json') - use_gpu (boolean, optional): Use GPU for inference if available (default: false) Returns: For JSON format: { "score": number, // Quality score 0-1 "errors": [ // Detected errors { "text": string, "start": number, "end": number, "severity": "minor" | "major" | "critical" } ], "summary": string // Human-readable summary } Examples: - Evaluate EN→JA translation quality - Check if MT output needs post-editing - Compare translation against reference