Server data from the Official MCP Registry
Create flashcard sets in the Vocabit app and read back how the learner actually did.
About
Create flashcard sets in the Vocabit app and read back how the learner actually did.
Security Report
Valid MCP server (3 strong, 2 medium validity signals). 1 code issue detected. No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
6 files analyzed · 2 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
What You'll Need
Set these up before or after installing:
Environment variable: VOCABIT_BASE_URL
Environment variable: VOCABIT_AGENT_KEY
Environment variable: VOCABIT_USER_ID
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-johnbilousov-vocabit-mcp": {
"env": {
"VOCABIT_USER_ID": "your-vocabit-user-id-here",
"VOCABIT_BASE_URL": "your-vocabit-base-url-here",
"VOCABIT_AGENT_KEY": "your-vocabit-agent-key-here"
},
"args": [
"-y",
"vocabit-mcp"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
vocabit-mcp
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:
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:
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:
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 mcp add vocabit -- npx -y vocabit-mcp
{
"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
| 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 — 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.
Card states
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.
Live mode
Point the server at a Vocabit backend that has the agent API enabled:
export VOCABIT_BASE_URL=https://your-vocabit-backend.example.com
export VOCABIT_AGENT_KEY=... # must match one of AGENT_API_KEYS on the backend
npx -y vocabit-mcp
| 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 — 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
git clone https://github.com/JohnBilousov/vocabit-mcp && cd vocabit-mcp
npm install
npm run build
npm test # tool surface + full loop, plus the HTTP client against a mocked fetch
npm run lint # eslint
npm run format # prettier --write
npm 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 mode
test/
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
Reviews
No reviews yet
Be the first to review this server!
More Developer Tools MCP Servers
Git
Freeby Modelcontextprotocol · Developer Tools
Read, search, and manipulate Git repositories programmatically
Fetch
Freeby Modelcontextprotocol · Developer Tools
Web content fetching and conversion for efficient LLM usage
Toleno
Freeby Toleno · Developer Tools
Toleno Network MCP Server — Manage your Toleno mining account with Claude AI using natural language.
mcp-creator-python
Freeby mcp-marketplace · Developer Tools
Create, build, and publish Python MCP servers to PyPI — conversationally.
MCP Marketplace
Freeby mcp-marketplace · Developer Tools
Search and install MCP servers from inside your AI client.
MarkItDown
Freeby Microsoft · Content & Media
Convert files (PDF, Word, Excel, images, audio) to Markdown for LLM consumption
