Vocabit

io.github.JohnBilousov由 GitHub 帳號 JohnBilousov 發布v0.1.3更新於 Oct 24, 2026

Create flashcard sets in the Vocabit app and read back how the learner actually did.

已驗證STDIO僅桌面效率與工作流知識與記憶

概覽

AI 產生的概覽

把助理連接到 Vocabit 單字卡應用程式,可將學習卡片組發布到學習者手機,並讀回其實際學習表現。

可以做什麼
  • 檢查連線狀態以及伺服器目前執行模式
  • 建立、列出、讀取與更新學習卡片組,並回傳可在裝置上開啟該組的深層連結
  • 讀取每張卡片的結果:困難卡、未觸及卡、到期卡、複習次數與完成情況
  • 透過 Telegram 通知學習者有新的卡片組待學習
  • 刪除卡片組,同時保留學習歷史
  • 以可列出的資源形式提供卡片組,並提供走完整學習循環的提示詞
適用情境
適合讓助理把某個主題做成單字卡,由真實學習者在 Vocabit 應用程式中學習,再依據應用程式的間隔重複結果調整下一組卡片,而不是憑空猜測。在還沒有後端或憑證時,可用示範模式先體驗完整循環。
執行需求
透過 npx 在本機執行,需要 Node.js。即時模式需要一個已啟用 agent API 的 Vocabit 後端:VOCABIT_BASE_URL 與 VOCABIT_AGENT_KEY(以 X-Agent-Key 傳送)。選用:VOCABIT_USER_ID、語言預設值、VOCABIT_TELEGRAM_ID、VOCABIT_TIMEOUT_MS。兩者都不設定則進入示範模式;只設定其中一個會拒絕啟動。
安裝前請注意
agent key 是密鑰,必須與後端設定的密鑰相符。伺服器會寫入真實應用程式:建立、更新與刪除都會改變學習者的卡片組,刪除被標記為破壞性操作,但學習歷史會保留。notify_learner 會透過 Telegram 傳訊息給真人,應謹慎使用。示範模式的結果來自模擬學習者,並會標示為模擬資料,並非真實資料。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Vocabit,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

vocabit-mcp

[CI] [npm] [license]

An MCP server for Vocabit, a flashcard app. It lets an AI assistant write a study set into a real app on a real phone, and then read back how the learner actually did with it.

Most MCP servers read from an API. This one closes a loop:

mermaid
flowchart LR    A["Assistant<br/>teaches a topic"] --> B["create_study_set"]    B --> C["Set appears in the<br/>Vocabit app"]    C --> D["Learner works<br/>through it"]    D --> E["get_set_results"]    E -->|weak cards| A

The interesting tool is not create_study_set — anything can generate flashcards. It is get_set_results: which cards the learner marked hard, which they never reached, how many reviews each one took. The next set is built out of that, not out of a guess.

Try it in 30 seconds

No backend, no account, no API key:

bash
npx -y vocabit-mcp --demo

Demo mode runs the same server against an in-memory Vocabit with two seeded sets. Create a set, ask for results, and a deterministic stand-in learner will have worked through it — flagged in the response as simulated, so it is never mistaken for real data.

To poke at it with a UI:

bash
npx @modelcontextprotocol/inspector npx -y vocabit-mcp --demo

Install

Listed in the MCP Registry as io.github.JohnBilousov/vocabit-mcp, so clients that read the registry can find it on their own.

Claude Code
bash
claude mcp add vocabit -- npx -y vocabit-mcp
Claude Desktop / any MCP client
json
{  "mcpServers": {    "vocabit": {      "command": "npx",      "args": ["-y", "vocabit-mcp"],      "env": {        "VOCABIT_BASE_URL": "https://your-vocabit-backend.example.com",        "VOCABIT_AGENT_KEY": "your-agent-key"      }    }  }}

Drop the env block to run in demo mode.

Tools

ToolWhat it does
vocabit_healthCheck the connection and which mode the server is in.
create_study_setPublish a set to the learner's app. Returns a deep link that opens it on the device.
list_study_setsRecent sets, newest first, each with a progress summary.
get_study_setFull contents of one set, plus the topic and notes the assistant attached.
get_set_resultsThe feedback half. Per-card status, weakCards, untouchedCards, due cards.
update_study_setRetitle, retag, or append cards — typically the follow-up after reading results.
notify_learnerTelegram ping that a set is waiting.
delete_study_setRemove a set from the app. Study history is kept.

Also exposed: the vocabit://set/{setId} resource (a set as JSON, listable) and a study-session prompt that walks the whole loop.

Card states

Progress comes from the app's spaced-repetition engine, not from the assistant:

StatusMeaning
newNever reviewed.
strugglingLearner marked it hard.
learningMarked good.
masteredMarked easy.

A set reports completed: true once no card is left in new.

Live mode

Point the server at a Vocabit backend that has the agent API enabled:

bash
export VOCABIT_BASE_URL=https://your-vocabit-backend.example.comexport VOCABIT_AGENT_KEY=...   # must match one of AGENT_API_KEYS on the backendnpx -y vocabit-mcp
VariablePurpose
VOCABIT_BASE_URLBackend base URL.
VOCABIT_AGENT_KEYSent as X-Agent-Key.
VOCABIT_USER_IDFirebase UID of the learner. Optional; the backend has a default.
VOCABIT_TERM_LANGUAGE / VOCABIT_DEFINITION_LANGUAGEDefaults for new sets, e.g. de / en.
VOCABIT_TELEGRAM_IDRecipient for notify_learner.
VOCABIT_TIMEOUT_MSRequest timeout, default 20000.
VOCABIT_DEMO1 forces demo mode.

Set neither URL nor key and the server starts in demo mode. Set exactly one and it refuses to start — half a configuration is a mistake, not a hint.

Design notes

Demo mode is a first-class client, not a stub. HttpVocabitClient and DemoVocabitClient implement the same VocabitClient interface, so no tool has a branch for "are we pretending?". A reviewer can run the server before they have credentials, and the test suite exercises the real tool surface over a real MCP transport rather than mocking the SDK.

Errors are recoverable, not fatal. A failed call comes back as isError with the backend's own message plus a hint aimed at the model — 404 says "call list_study_sets to see which sets exist", 401 says "or run with VOCABIT_DEMO=1". Mutually exclusive arguments are rejected with an explanation instead of a guess.

Output schemas stay loose on the edges. Identifying fields are required; everything else is optional, so a backend that grows a field does not turn a working tool into a validation error.

Annotations are honest. delete_study_set is marked destructiveHint, the read tools readOnlyHint. notify_learner messages a real person, and its description says to use it sparingly.

Development

bash
git clone https://github.com/JohnBilousov/vocabit-mcp && cd vocabit-mcpnpm installnpm run buildnpm test          # tool surface + full loop, plus the HTTP client against a mocked fetchnpm run lint      # eslintnpm run format    # prettier --writenpm run inspect   # demo mode in the MCP Inspector

CI runs typecheck, lint, format:check, test, and build on every push and pull request.

src/  index.ts        CLI entry, stdio transport  config.ts       env → Config, demo-mode resolution  server.ts       tool / resource / prompt registration  schemas.ts      zod input and output shapes  format.ts       human-readable summaries next to structuredContent  client/    types.ts      wire types + VocabitClient contract    http.ts       live backend    mock.ts       in-memory backend for demo modetest/  server.test.ts       tool surface + full loop — over an in-memory MCP transport  client/    http.test.ts       query encoding, error-body parsing, timeouts — against a mocked fetch

Releasing

Publishing uses npm's trusted publishing (OIDC) — no NPM_TOKEN secret, nothing that can leak or expire. One-time setup on npmjs.com, under the package's Settings → Trusted publishing → GitHub Actions: organization JohnBilousov, this repository, workflow filename publish.yml.

To cut a release: bump the version in package.json, server.json, and VERSION in src/server.ts together (a test asserts they can't drift), commit, push, then publish a GitHub Release with a matching vX.Y.Z tag. That triggers .github/workflows/publish.yml, which runs the test suite and publishes to npm with provenance — the package page shows a verified link back to this exact commit and workflow run, not just a name on the registry.

Roadmap

  • Streamable HTTP transport alongside stdio
  • Multi-learner support without a backend default UID
  • Audio pronunciation cards

License

MIT © Ivan Bilousov

來源:README.md,提交 b0bc8fd

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v0.1.3最新Sep 16, 2026

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