TranslateGemma MCP Server
Enables on-device text translation via a local TranslateGemma LLM API, supporting multiple language pairs with automatic retries and rate limiting for privacy and low latency.
README
🌐 TranslateGemma MCP Server
A Model Context Protocol (MCP) server that provides high-quality text translation via the local TranslateGemma LLM API. Designed for low-latency, private, and secure on-device translation with full control over the underlying model.
✨ Features
- Local Translation Inference: Leverages TranslateGemma (or compatible OpenAI-like API) on your machine — no external data leaves your device.
- Multi-Language Support: Translate between any language pairs using standardized BCP-47 codes (
en,de-DE,fr-FR,ja-JP, etc.). - Automatic Retries & Rate Limiting: Built-in exponential backoff and 30 req/min rate limiting for stability.
- Robust Error Handling: Graceful failures with clear, actionable error messages.
- Browser/Client Friendly: CORS-enabled HTTP endpoint for direct integration.
- LLM-Ready Output: Clean, structured responses optimized for tool-use workflows.
🛠️ Requirements
- Python 3.9+
fastmcp,uvicorn,httpx, andstarlette- A local TranslateGemma-compatible server (e.g., running via
ollama,vllm, or custom server) exposing an OpenAI/v1/chat/completionsendpoint.
pip install fastmcp uvicorn httpx starlette
🚀 Installation & Usage
1. Clone & Prepare
git clone https://github.com/your-username/translategemma-mcp-server.git
cd translategemma-mcp-server
2. Start TranslateGemma Backend
Ensure your TranslateGemma API is accessible at http://127.0.0.1:8080/v1/chat/completions (or configure via --api-url).
llama-server -ngl 99 -m translategemma-27b-it.Q6_K.gguf -c 8000 --no-jinja
3. Launch the MCP Server
python3 server.py --host 0.0.0.0 --port 3000
Or override the API endpoint:
python3 server.py --api-url http://192.168.1.100:8000/v1/chat/completions
The server exposes an MCP-compatible HTTP endpoint at http://localhost:3000/mcp.
🧰 Available Tools
translate
Translates text using TranslateGemma with intelligent language handling.
async def translate(
text: str,
source_lang_code: str,
target_lang_code: str,
ctx: Context,
max_retries: int = 2,
) -> str
Parameters
| Name | Type | Description |
|---|---|---|
text |
str |
Required. The text to translate (up to ~2k chars recommended). |
source_lang_code |
str |
Required. Source language code (e.g., "en", "auto", "zh-Hans"). |
target_lang_code |
str |
Required. Target language code (e.g., "de-DE", "fr-FR", "ja-JP"). |
ctx |
Context |
MCP context (auto-injected). Used for logging. |
max_retries |
int |
Retry attempts on transient failures (default: 2). |
Returns
- ✅ Translated text (clean, trimmed)
- ❌ Error message (prefixed with
❌/⚠️) on failure
⚙️ Configuration
| CLI Flag | Environment | Default | Description |
|---|---|---|---|
--host |
HOST |
127.0.0.1 |
Server host (use 0.0.0.0 for external access) |
--port |
PORT |
3000 |
Server port |
--api-url |
API_URL |
http://127.0.0.1:8080/v1/chat/completions |
TranslateGemma API endpoint |
🔒 Security & Privacy
- All translation is performed locally on your machine.
- No telemetry, external tracking, or data collection.
- CORS configured permissively (
*) for local dev — restrict origins in production.
📄 License
MIT License — see LICENSE for details.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。