Create flashcard sets in the Vocabit app and read back how the learner actually did.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
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:
The interesting tool is not create_study_set β 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.
No backend, no account, no API key:
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 β flagged in the response as simulated, so it is never mistaken for real data.
To poke at it with a UI:
Listed in the MCP Registry as io.github.JohnBilousov/vocabit-mcp, so clients that read the registry can find it on their own.
Drop the env block to run in demo mode.
| Tool | What it does |
|---|---|
vocabit_health | Check the connection and which mode the server is in. |
create_study_set | Publish a set to the learner's app. Returns a deep link that opens it on the device. |
list_study_sets | Recent sets, newest first, each with a progress summary. |
get_study_set | Full contents of one set, plus the topic and notes the assistant attached. |
get_set_results | The feedback half. Per-card status, weakCards, untouchedCards, due cards. |
update_study_set | Retitle, retag, or append cards β typically the follow-up after reading results. |
notify_learner | Telegram ping that a set is waiting. |
delete_study_set | Remove 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.
Progress comes from the app's spaced-repetition engine, not from the assistant:
| Status | Meaning |
|---|---|
new | Never reviewed. |
struggling | Learner marked it hard. |
learning | Marked good. |
mastered | Marked easy. |
A set reports completed: true once no card is left in new.
Point the server at a Vocabit backend that has the agent API enabled:
| Variable | Purpose |
|---|---|
VOCABIT_BASE_URL | Backend base URL. |
VOCABIT_AGENT_KEY | Sent as X-Agent-Key. |
VOCABIT_USER_ID | Firebase UID of the learner. Optional; the backend has a default. |
VOCABIT_TERM_LANGUAGE / VOCABIT_DEFINITION_LANGUAGE | Defaults for new sets, e.g. de / en. |
VOCABIT_TELEGRAM_ID | Recipient for notify_learner. |
VOCABIT_TIMEOUT_MS | Request timeout, default 20000. |
VOCABIT_DEMO | 1 forces demo mode. |
Set neither URL nor key and the server starts in demo mode. Set exactly one and it refuses to start β half a configuration is a mistake, not a hint.
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 β 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.
CI runs typecheck, lint, format:check, test, and build on every push and pull request.
Publishing uses npm's trusted publishing (OIDC) β
no NPM_TOKEN secret, nothing that can leak or expire. One-time setup on npmjs.com, under the
package's Settings β Trusted publishing β 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 β the
package page shows a verified link back to this exact commit and workflow run, not just a name on
the registry.
MIT Β© Ivan Bilousov
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