Anki MCP Server

Anki MCP Server bridges the gap between AI assistants and one of the most widely used spaced‑repetition tools, Anki. By exposing a set of intuitive MCP tools, the server lets assistants query and modify flashcard collections without leaving the conversational interface. This eliminates the need to manually open Anki or write custom scripts, allowing developers to build study‑support workflows that feel seamless and natural.

The server solves a common pain point for learners and educators: automating the creation, update, and analysis of flashcard decks. Instead of copying data between spreadsheets or writing Python snippets to use AnkiConnect, an AI can ask for a deck overview, add new notes in bulk, or retrieve daily review statistics—all through simple MCP calls. This streamlines the study‑cycle and keeps users focused on content rather than tooling.

Key capabilities are packaged as distinct tools:

• get‑collection‑overview provides a snapshot of decks, models, and fields, giving assistants context about the user’s existing study material.
• add‑or‑update‑notes supports both single and batched operations, enabling rapid expansion or correction of decks directly from a conversation.
• get‑cards‑reviewed exposes daily review counts, useful for tracking progress or generating reminders.
• find‑notes leverages Anki’s native search syntax, allowing precise queries for notes that match complex criteria.

These tools empower a variety of real‑world scenarios. A tutor can prompt an assistant to pull all notes matching “biology” and generate quiz questions on the fly. A language learner can ask for a summary of cards reviewed yesterday, while an educator could batch‑import new vocabulary from a lesson plan. Because the server relies on AnkiConnect, it preserves all standard Anki features—card scheduling, tagging, and media handling—while giving AI agents a programmatic interface.

Integration is straightforward: developers add the MCP server to their AI client’s configuration, and the assistant automatically discovers the available tools. Once connected, a developer can compose prompts that invoke these tools, embed tool outputs in responses, or chain multiple calls to build sophisticated study‑automation pipelines. The result is a fluid workflow where the AI acts as an intelligent study companion, seamlessly interacting with Anki behind the scenes.