MCP Translation Server

What is MCP Translation Server?

MCP Translation Server is a high-performance machine translation system specialized for Manchu-Chinese bidirectional translation, combining advanced linguistic processing and deep learning for low-resource language translation.

How to use MCP Translation Server?

Clone the repository, set up a Python virtual environment (Python 3.12+), install dependencies, copy and edit config.example.py to config.py, and set required environment variables (MCP_SECRET_KEY, MCP_API_TOKEN). Run python server.py to start the server, or run python demo/comprehensive_demo.py for a demo.

Key features of MCP Translation Server

• Enhanced morphological analysis with vowel harmony and error detection
• Multi-level translation engine with intelligent corpus matching
• Rich language resources (rules, parallel corpus, dictionary)
• REST API endpoints for translation and morphological analysis
• Performance: average latency <1s, cache hit rate >80%
• Monitoring via Prometheus and Grafana

Use cases of MCP Translation Server

• Bidirectional translation between Manchu and Chinese
• Morphological analysis of Manchu text (word form, vowel harmony)
• Batch processing of parallel corpus data
• Real-time translation with high concurrency (100+ req/s)

FAQ from MCP Translation Server

What runtime dependencies does MCP Translation Server require?

Python 3.12+ is required. Docker is supported. The server also needs a config.py file and the environment variables MCP_SECRET_KEY and MCP_API_TOKEN (optional: MCP_REDIS_PASSWORD, MCP_SMTP_PASSWORD).

How do I configure the server?

Copy config.example.py to config.py and edit the settings (API host/port, rate limiting, caching, model paths, etc.). Never commit config.py to version control.

What API endpoints are available?

POST /api/v1/translate for translation (with text, source_lang, target_lang) and POST /api/v1/analyze for morphological analysis.

What data does MCP Translation Server use?

It uses bundled language resources: Manchu grammar rules (manchu_rules.json), a parallel corpus (parallel_corpus.json), and a dictionary (dictionary.json).

How is performance measured?

Average translation latency is under 1 second, 95th percentile under 2 seconds, and the server handles over 100 concurrent requests per second.