com.k2kanji/study · v1.1.0 · productivity
K2 Study Decks & Algorithm is healthy: it completed an MCP handshake in 1147 ms and exposes 7 tools.
Study decks for 8 languages plus the K2 spaced-repetition engine, runnable on your items.
Endpoint: https://k2kanji.com/api/mcp (streamable-http)
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| Users (last 30 days) | 0 |
| Handshake | Healthy |
| Auth needed | None |
| Tool count | 7 |
| Latency | 1147 ms |
| Last checked (UTC) | 2026-10-23T17:15:15.175Z |
| Checked (UTC) | Status | Latency | Error |
|---|---|---|---|
| 2026-10-23T17:15:15.175Z | Healthy | 1147 |
list_decks | The catalog of K2 study decks: eight curated datasets spanning Japanese kanji and vocabulary, HSK Chinese, Korean Hangul, Spanish, French, classical Latin and medical Latin. Returns each deck id (the value other tools take), what it teaches, how many entries it has, and what its fields mean. Call th |
search_deck | Search one deck. `query` substring-matches the term, its reading/pronunciation, its English meanings, and its category, so "bone" finds entries glossed as bone and "ㄱ" finds that jamo. Narrow with `category` (exact, from list_decks) or `difficulty` (1-5; for the kanji deck this is the school grade). |
get_entry | One deck entry in full, matched on the exact term (the kanji, hanzi, jamo, or word itself). Case-insensitive. Returns every field the deck carries, including the deck-specific extras: stroke counts and JLPT level for kanji, component kanji for compounds, declension and English derivatives for Latin, |
get_cube_algorithms | Rubik's cube last-layer algorithm sets used by the K2 cube trainer: full OLL (57 cases), full PLL (21), the two-look beginner subsets, and full ZBLL. Each case returns its primary algorithm plus alternates, with sources and finger-trick notes where recorded. Every algorithm in these sets is verified |
explain_k2_algorithm | The K2 spaced-repetition engine, described. K2 stores no review dates: each item holds a probability p that halves on a correct answer and doubles on a wrong one, and items are drawn by Monte Carlo rejection sampling, so the pool self-organizes around current weaknesses and expands when it cools. Re |
simulate_k2_session | Run the real K2 engine over your own items. Give it a list of terms (any strings — your flashcards, cube cases, anything), a study mode, and a sequence of answers, and it returns the p-value trajectory per answer, which items are now mastered, when the pool expanded, and the probability distribution |
get_site_info | What K2 is, which apps it runs, what this server does and does not expose, and every machine-readable surface. Also returns the full capability map — every tool, prompt, and resource — so a client with a stale or tools-only roster can still see the whole surface. |