xCOMET MCP Server

Name: xCOMET MCP Server
Author: shuji-bonji

@shuji-bonji

xCOMET MCP Server について

xCOMET MCP Server provides AI agents with the ability to evaluate machine translation quality. It integrates with the xCOMET model from Unbabel to provide:

#xcomet #machine-translation #quality-evaluation

基本情報

カテゴリ

その他

トランスポート

stdio

公開者

shuji-bonji

投稿者

shuji narumi

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "xcomet": {
      "command": "npx",
      "args": [
        "-y",
        "xcomet-mcp-server"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is xCOMET MCP Server?

xCOMET MCP Server is a translation quality evaluation server powered by the xCOMET model from Unbabel. It provides AI agents with quality scoring, error detection, and batch processing capabilities for machine translation.

How to use xCOMET MCP Server?

Install the server via npm and configure it in your MCP host (e.g., Claude Desktop) using the command npx -y xcomet-mcp-server. Use the provided tools (xcomet_evaluate, xcomet_detect_errors, xcomet_batch_evaluate) to evaluate translation quality. Optionally set environment variables for model selection, transport mode (stdio or http), and GPU acceleration.

Key features of xCOMET MCP Server

Quality scoring on a 0–1 scale
Error detection with minor, major, and critical severity levels
Batch evaluation of up to 500 translation pairs
GPU support for accelerated inference
Persistent model loading for up to 177x speedup on consecutive requests
Designed to integrate with other MCP servers like DeepL

Use cases of xCOMET MCP Server

Evaluate machine translation quality for single or batch translations
Detect and categorize translation errors with severity ratings
Combine with translation MCP servers to create iterative quality workflows
Quality assurance and post-editing guidance for localization projects

FAQ from xCOMET MCP Server

What models are supported and what are their memory requirements?

Three models are supported: Unbabel/XCOMET-XL (3.5B parameters, ~14GB size, ~8–10GB RAM), Unbabel/XCOMET-XXL (10.7B parameters, ~42GB size, ~20GB RAM), and Unbabel/wmt22-comet-da (580M parameters, ~2GB size, ~3GB RAM). The latter requires a reference translation.

How does the persistent server architecture work?

The server keeps the xCOMET model loaded in memory using an internal Python FastAPI server. The first request loads the model (25–90 seconds), subsequent requests run in ~500ms. Optionally, set XCOMET_PRELOAD=true to load the model at startup.

What transport modes are available?

The server supports stdio (default) and http transport modes. Set the TRANSPORT environment variable to http and optionally configure PORT (default 3000) for remote access.

How do I specify the Python executable path?

Set the XCOMET_PYTHON_PATH environment variable to an explicit Python path. Otherwise, the server auto-detects Python from pyenv, Homebrew, or the system path.

What does the quality score mean?

Score Range	Quality	Recommendation
0.9 – 1.0	Excellent	Ready for use
0.7 – 0.9	Good	Minor review recommended
0.5 – 0.7	Fair	Post-editing needed
0.0 – 0.5	Poor	Re-translation recommended

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