There’s an Anki addon called anki-mcp-server that exposes your Anki collection over MCP (Model Context Protocol). If you haven’t come across MCP yet — it’s basically a standardized way for AI assistants to interact with external tools. Connect this addon and suddenly Claude (or whatever you’re using) can search your cards, create notes, browse your decks, etc.
Pretty cool, but there was a big gap: zero FSRS support. The assistant could see your cards but had no idea how they were being scheduled. It couldn’t read your FSRS parameters, couldn’t check a card’s memory state, couldn’t run the optimizer. For anyone who’s moved past SM-2 (which should be everyone at this point), that’s a significant blind spot.
So I wrote a PR that adds four tools and a resource to fill that gap.
get_fsrs_params reads the FSRS weights, desired retention, and max interval — either for all presets at once or filtered to a specific deck. get_card_memory_state pulls the per-card memory state (stability, difficulty, retrievability) for a given card ID. set_fsrs_params lets you update retention or weights on a preset. And optimize_fsrs_params runs Anki’s built-in optimizer — with a dry-run mode so you can preview the optimized weights before committing.
There’s also an anki://fsrs/config resource that gives a quick overview of your FSRS setup without needing a tool call.
The annoying part was version compatibility. FSRS has been through several iterations and Anki stores the parameters under different protobuf field names depending on which version you’re on. I ended up writing a fallback chain that tries fsrsParams6 first, then fsrsParams5, then fsrsParams4, and finally the old fsrsWeights field. The optimizer tool also needs to adjust its kwargs depending on the Anki point version (25.02 and 25.07 changed the interface). All of that version-detection logic lives in a shared _fsrs_helpers.py so the individual tools stay clean.
One gotcha that took me a bit to track down: per-deck desired retention overrides are stored as 0–100 on the deck config dictionary, but the preset stores them as 0–1. Easy to miss, and you’d get nonsensical results if you didn’t normalize between the two.
What I’m most excited about is what this enables in practice. You can now ask an AI assistant things like “run the optimizer on my medical deck in dry-run mode and tell me how the new weights compare” or “which of my presets has the lowest desired retention?” — and it can actually do it, pulling real data from your collection instead of just guessing. For someone who spends a lot of time tweaking FSRS settings across different decks, having that accessible through natural language is a nice quality-of-life improvement.
The PR was recently merged. I tested everything locally — built the .ankiaddon, installed it in Anki, ran through all the tools against a live collection. If you’re into the Anki + AI workflow, take a look and let me know what you think.
/root 