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.
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