@shuji-bonji
基本情報
xCOMET MCP Server provides AI agents with the ability to evaluate machine translation quality. It integrates with the xCOMET model from Unbabel to provide:
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"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。
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.
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.
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.
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.
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.
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.
| 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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