Building an MCP Server for Language Learning

Published on February 24, 2026

When we set out to build the RURussian MCP server, our goal was to bridge the gap between powerful general-purpose LLMs and highly specific linguistic data. Here is a look behind the scenes at how we designed the architecture.

The Challenge

Russian grammar is complex. LLMs are great at generating text, but they sometimes hallucinate specific grammar rules or vocabulary declensions. We needed a way to ground the AI's responses in our verified database.

The Solution: Model Context Protocol (MCP)

MCP allows us to expose our backend APIs directly to AI agents like OpenClaw bots. Instead of relying on the model's internal knowledge, the bot can call our tools:

• get_word_data queries our database for exact morphological information.
• get_sentences retrieves human-verified example sentences.
• analyze_sentence uses our proprietary NLP pipeline to break down Russian syntax.

Authentication and Security

We implemented a straightforward authenticate tool. This ensures that only users with a valid API key can access our premium linguistic endpoints, while keeping the integration process seamless for bot developers.

The result is a fast, reliable bridge that turns any capable AI into an expert Russian tutor. Developers can view our full API on the API Documentation page.