Japanese Vocab Anki MCP Server

Overview

The Japanese Vocab Anki MCP Server bridges language‑model assistants with the powerful spaced‑repetition engine of Anki, enabling developers to programmatically manage and enrich Japanese vocabulary decks. By exposing a rich set of resources, tools, and prompts through the Model Context Protocol, the server turns Anki into a dynamic learning backend that can be queried, updated, and analyzed from within any Claude‑compatible AI workflow. This eliminates the need for manual card creation or spreadsheet manipulation, allowing a single conversational interface to handle everything from importing new words to tracking progress.

At its core, the server offers a clean REST‑like API over MCP: lists all decks, while retrieves the cards within a specific deck. Specialized endpoints such as and provide quick snapshots of recent study activity, making it trivial for a model to surface “today’s new words” or “cards that need a refresher.” These resources are complemented by tools that perform state‑changing operations—adding cards, reviewing them with a specified ease factor, or pulling detailed review history for analytics.

The Japanese‑specific tooling is where this server truly shines. It expects a note type called “Japanese (recognition)” and offers , which pulls a CSV of words, readings, and meanings straight into the deck. More importantly, it supplies , allowing an assistant to generate contextual sample sentences (via the provided prompts) and inject them into the Reading field. This workflow transforms a bare‑bones vocabulary list into a richly annotated study set, dramatically improving retention by providing natural usage examples.

Real‑world use cases include:

• Automated curriculum building – A language‑learning platform can import a semester’s worth of words, let an AI generate practice sentences, and push them into Anki for students to review.
• Adaptive spaced repetition – A system can query recent reviews, identify words that need extra exposure, and ask the model to generate new fill‑in‑the‑blank exercises on the fly.
• Progress analytics – Developers can pull review history, compute metrics like average ease or decay rates, and surface actionable insights to learners or instructors.

Integration with AI workflows is straightforward: a Claude prompt can request the , receive a list of words, use to format sample sentences, and finally call . The MCP server handles all database interactions, freeing the model to focus on natural language generation and user intent. This tight coupling between AI and Anki unlocks a seamless, scalable study loop that would otherwise require manual editing or custom scripting.