xCOMET MCP Server
About
Translation quality evaluation using xCOMET models. Provides quality scoring (0-1), error detection with severity levels, and optimized batch processing with 25x speedup.
Details
- Author
- shuji-bonji
- Downloads
- 240
- Categories
- Other, AI
Jump to
- 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
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
xCOMET MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"xcomet mcp server": {
"xcomet": {
"command": "npx",
"args": [
"-y",
"xcomet-mcp-server"
]
}
}
}
}
McpServers
{
"xcomet": {
"command": "npx",
"args": [
"-y",
"xcomet-mcp-server"
]
}
}
xCOMET MCP Server
🎯 Overview
xCOMET MCP Server provides AI agents with the ability to evaluate machine translation quality. It integrates with the xCOMET model from Unbabel to provide: - Quality Scoring: Scores between 0-1 indicating translation quality - Error Detection: Identifies error spans with severity levels (minor/major/critical) - Batch Processing: Evaluate multiple translation pairs efficiently (optimized single model load) - GPU Support: Optional GPU acceleration for faster inference ``mermaid
graph LR
A[AI Agent] --> B[Node.js MCP Server]
B --> C[Python FastAPI Server]
C --> D[xCOMET Model<br/>Persistent in Memory]
D --> C
C --> B
B --> A
style D fill:#9f9
`
🔧 Prerequisites
Python Environment
xCOMET requires Python with the following packages:
`bash
pip install "unbabel-comet>=2.2.0" fastapi uvicorn
`
Model Download
The first run will download the xCOMET model (~14GB for XL, ~42GB for XXL):
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