Anki‑MCP: Bridging AI Assistants and Flashcard Mastery

Anki‑MCP turns the powerful, locally‑hosted Anki desktop application into a first‑class API that AI assistants can call through the Model Context Protocol. By exposing every AnkiConnect action as an MCP tool, it solves a common pain point for developers: integrating spaced‑repetition learning into conversational agents without exposing the user’s local data or writing custom adapters. Instead of building bespoke integrations for each language model, developers can simply register the anki server in their MCP client configuration and start invoking flashcard operations with a familiar tool‑based syntax.

The server groups tools into logical services—deck, note, card, and model—mirroring AnkiConnect’s own naming conventions. This structure lets developers browse capabilities with the MCP Inspector, see which actions are available for a given deck or note type, and construct complex workflows. For example, an AI tutor could query to locate all cards matching a user’s study goal, then use or to adjust review schedules on the fly. Because each tool maps directly to an AnkiConnect RPC, there is no latency penalty beyond the local HTTP call; the MCP server simply forwards parameters and returns results, preserving Anki’s native error handling.

Key features include:

• Full deck management: Create, rename, delete, and reconfigure decks with tools.
• Fine‑grained note control: Add, update, delete, and tag notes via actions.
• Card lifecycle operations: Suspend, unsuspend, and query card states using tools.
• Model introspection: Retrieve model definitions, field names, and template content with tools.
• Seamless integration: Add the server to any MCP‑compatible client by specifying a simple command configuration; no manual API keys or network setup required.

Real‑world scenarios that benefit from Anki‑MCP include:

• Personalized tutoring bots that adjust card difficulty in real time based on conversation cues.
• Study‑assistant integrations where a language model can pull up the next card, explain its content, and then automatically mark it as known or unknown.
• Knowledge‑base enrichment where an AI assistant imports new flashcards from external sources (e.g., parsed PDFs) and organizes them into appropriate decks.
• Productivity tools that surface a user’s most recently reviewed cards or suggest new review sessions during idle periods.

By abstracting Anki’s complex JSON API behind a clean, tool‑centric interface, Anki‑MCP empowers developers to weave spaced repetition into AI workflows effortlessly. Its tight coupling with local Anki data ensures privacy and speed, making it an ideal companion for any project that wants to combine conversational intelligence with evidence‑based learning.