MCP server

K2 Study Decks & Algorithm

com.k2kanji/study

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Study decks for 8 languages plus the K2 spaced-repetition engine, runnable on your items.

Website

At a glance

Type

MCP server

Publisher

k2kanji.com (verified by the MCP Registry)

Status

Working at last check

Tools

7

Token cost

About 1,538 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http study https://k2kanji.com/api/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.study]
url = "https://k2kanji.com/api/mcp"

Gemini CLI

{"mcpServers":{"study":{"httpUrl":"https://k2kanji.com/api/mcp"}}}

Cursor

{"mcpServers":{"study":{"url":"https://k2kanji.com/api/mcp"}}}

VS Code

{"servers":{"study":{"type":"http","url":"https://k2kanji.com/api/mcp"}}}

Antigravity

agy mcp add study https://k2kanji.com/api/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"study","server_url":"https://k2kanji.com/api/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"study","server_url":"https://k2kanji.com/api/mcp"}

Tools

• explain_k2_algorithm ~173 tokens
  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
• get_cube_algorithms ~278 tokens
  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
• get_entry ~195 tokens
  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_site_info ~101 tokens
  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.
• list_decks ~117 tokens
  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 ~300 tokens
  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).
• simulate_k2_session ~374 tokens
  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

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