Lara MCP vs Xcomet MCP Server

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

xcomet_detect_errors

Detect and categorize errors in a translation. This tool focuses on error detection, providing detailed information about translation errors with their severity levels and positions. Args: - source (string): Original source text - translation (string): Translated text to analyze - reference (string, optional): Reference translation - min_severity ('minor' | 'major' | 'critical'): Minimum severity to report (default: 'minor') - response_format ('json' | 'markdown'): Output format (default: 'json') - use_gpu (boolean, optional): Use GPU for inference if available (default: false) Returns: { "total_errors": number, "errors_by_severity": { "minor": number, "major": number, "critical": number }, "errors": [ { "text": string, "start": number, "end": number, "severity": "minor" | "major" | "critical" } ] } Examples: - Find critical errors before publication - Identify areas needing post-editing - Quality gate for MT output

xcomet_batch_evaluate

Evaluate multiple translation pairs in a batch. This tool processes multiple source-translation pairs and provides aggregate statistics along with individual results. Args: - pairs (array): Array of translation pairs, each with: - source (string): Original source text - translation (string): Translated text - reference (string, optional): Reference translation - source_lang (string, optional): Source language code - target_lang (string, optional): Target language code - response_format ('json' | 'markdown'): Output format (default: 'json') - use_gpu (boolean, optional): Use GPU for inference if available (default: false) - batch_size (number, optional): Inference batch size, 1-64 (default: 8). Larger = faster but uses more memory. Returns: { "average_score": number, "total_pairs": number, "results": [ { "index": number, "score": number, "error_count": number, "has_critical_errors": boolean } ], "summary": string } Examples: - Evaluate entire translated document - Compare MT system quality across test set - Identify segments needing attention