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Create and study EngrAmo spaced-repetition flashcards from your AI client.
About
Create and study EngrAmo spaced-repetition flashcards from your AI client.
Remote endpoints: streamable-http: https://mcp.engramo.app/
Security Report
Valid MCP server (2 strong, 2 medium validity signals). No known CVEs in dependencies. ⚠️ Package registry links to a different repository than scanned source. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
Endpoint verified · Requires authentication · 2 issues found
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What You'll Need
Set these up before or after installing:
Environment variable: ENGRAMO_API_TOKEN
Environment variable: ENGRAMO_API_URL
Environment variable: ENGRAMO_TTS_GEMINI_API_KEYS
How to Install & Connect
Available as Local & Remote
This plugin can run on your machine or connect to a hosted endpoint. during install.
Documentation
View on GitHubFrom the project's GitHub README.
engramo-mcp
MCP server for EngrAmo, the spaced-repetition flashcard platform.
Hosted server
The hosted server at https://mcp.engramo.app/ needs no installation. It supports OAuth 2.0 with Dynamic Client
Registration: your client sends you to the EngrAmo sign-in page, you sign in with your EngrAmo email and password
and approve — no API token to paste. There is no sign-up on that page, so create your account in the EngrAmo app
first. EngrAmo is listed in the Claude Connectors Directory. Setup guides:
Claude and ChatGPT;
the public docs are at https://engramo.app/connect/. You can revoke access any time in the EngrAmo app under
Settings → Connected apps.
Set up with your AI assistant
Not a developer? An agentic AI app can do the whole setup for you, including card audio. Tested with Antigravity in auto mode; other apps that can run terminal commands and edit files (Claude Code, Codex, Cursor) should work the same way.
Prepare two keys first:
- EngrAmo API token: sign in at https://study.engramo.app, open Settings → API Tokens, and create one.
- Gemini API key (for card audio, free): create one at Google AI Studio.
Keep both somewhere you can copy them from. Never paste them into the chat; the setup asks for them in a private pop-up instead.
Then paste this prompt into your AI app:
Set up the EngrAmo MCP server for me, with card audio, following
https://github.com/engramo-developer/engramo-mcp. I'm not a developer, so do every step yourself and
tell me in plain words what you're doing.
1. Download the prebuilt engramo-mcp binary for my computer from the latest GitHub release
(https://github.com/engramo-developer/engramo-mcp/releases/latest/download/<file>, where <file> is
engramo-mcp-darwin-arm64, engramo-mcp-darwin-x64, engramo-mcp-linux-x64, engramo-mcp-linux-arm64
or engramo-mcp-win32-x64.exe). Save it to ~/.engramo/bin/ and make it executable. Don't install
Node.js or npm.
2. Add an "engramo" server to this app's MCP config file. Keep any servers already there. Use the
binary's full path as "command" and set three env values: ENGRAMO_API_URL=https://api.engramo.app,
ENGRAMO_API_TOKEN and ENGRAMO_TTS_GEMINI_API_KEYS.
3. For the two keys, never ask me to paste them into this chat. Ask for each key in my operating
system's own pop-up with a hidden input field (macOS: osascript "display dialog ... with hidden
answer"; Windows: Get-Credential in powershell.exe; Linux: zenity --password or kdialog --password),
and write the value straight into the config file from that same command. Never print, echo or
read back the keys. Only if no pop-up is possible, use a hidden terminal prompt instead. Use one
method at a time, and don't make me edit the config file.
4. Tell me how to reload the MCP servers in this app, and wait until I say done.
5. Check the connection: call get_server_version and list_tts_voices. If something fails, fix it.
6. When both work, say "Ready! What cards should we make?" with the server version.
To check the setup later (after an update, or if something stops working), paste:
Check my EngrAmo connection: call get_server_version, list_tts_voices and get_subscription_usage.
Tell me in plain words the server version, whether card audio is ready (and the default voice), my
plan, and how many cards I can still create. If any call fails, explain why and how to fix it.
When it says ready, ask for cards in your own words, for example:
Create a catalog "Job interview English" with 15 cards of useful interview phrases. Put the phrase on
the front and the meaning plus an example on the back, highlight the key words, and add audio to every
card.
More ready-to-paste prompts (cover images, adding audio to a deck, turning notes into cards, study plans and more): docs/prompt-examples.md.
Prerequisites
- An EngrAmo account
- An EngrAmo API token — generate one from your account's Settings → API Tokens page. Only needed for the local
(
stdio) setups below and for remote clients without OAuth; the hosted connectors use OAuth instead.
Environments
engramo-mcp is environment-agnostic — the binary itself has no baked-in "dev" or "prod." Which server it
talks to is controlled entirely by the ENGRAMO_API_URL you set in your client config, paired with a token
minted from that same environment:
| Environment | ENGRAMO_API_URL | MCP server URL (http transport only) | Who it's for |
|---|---|---|---|
| Production (default in every example below) | https://api.engramo.app | https://mcp.engramo.app | Real accounts — use this unless you have a specific reason not to |
| Dev | https://api-engram.volmyr.com | https://mcp-engramo.volmyr.com | EngrAmo team / internal testing only |
Dev and prod are separate backends with separate accounts. A token minted from one will not authenticate
against the other — if you switch ENGRAMO_API_URL, you must also swap in a token generated from that same
environment's Settings → API Tokens page.
ENGRAMO_API_URL vs the MCP server URL — these are not interchangeable. ENGRAMO_API_URL is the plain
EngrAmo backend; it's never something a client connects to directly. In stdio config (Claude Desktop,
Claude Code, Codex, VS Code, Cursor, Windsurf, Gemini CLI, Antigravity stdio below), the client sets
ENGRAMO_API_URL together with ENGRAMO_API_TOKEN, since the locally-launched binary calls the backend
itself. In http/remote config (Antigravity remote, ChatGPT), the
client instead points serverUrl at the MCP server's own URL (mcp.engramo.app / mcp-engramo.volmyr.com
above) and authenticates separately per session — via OAuth by default, or a static Authorization: Bearer
header if you'd rather skip the login prompt. ENGRAMO_API_URL still matters in http mode, but only as an
env var set by whoever deploys engramo-mcp http — a client never sets it.
Installation
Install the MCP server globally so it's available as a system command:
npm install -g @engramo/mcp
Prebuilt binaries ship for macOS (arm64, x64), Linux (arm64, x64) and Windows (x64). On other platforms, build
from source with cargo build --release.
Verify the installation:
engramo-mcp --version
Alternatively, skip installation entirely and use
npx -y @engramo/mcpin your client config — npm will download the binary automatically on first use.
Choosing a client
Any MCP client works. Local text-to-speech needs a stdio
(locally launched) connection, so remote-only clients never get it.
| Client | Models | Local TTS | Setup |
|---|---|---|---|
| Claude (claude.ai, Desktop, mobile) | Claude | ❌ — hosted connector only; local TTS intentionally unavailable | Claude (hosted connector) — recommended: install from the Connectors Directory, nothing to download |
| Claude Desktop extension | Claude | ✅ | Claude Desktop — use it for card audio: one-click extension, no Node.js or config editing; the free plan supports local extensions |
| Claude Code | Claude | ✅ | Claude Code |
| Codex (CLI / IDE extension) | OpenAI models — sign in with your ChatGPT account | ✅ | Codex |
| VS Code (GitHub Copilot) | GPT / Claude / Gemini | ✅ | VS Code |
| Cursor | GPT / Claude / Gemini | ✅ | Cursor |
| Windsurf | GPT / Claude / Gemini | ✅ | Windsurf |
| Gemini CLI | Gemini | ✅ | Gemini CLI |
| Antigravity | Gemini / Claude / GPT | ✅ stdio / ❌ remote | Antigravity |
| ChatGPT | OpenAI models | ❌ — remote connector only (OAuth); local TTS intentionally unavailable | ChatGPT |
Claude Desktop
Most Claude Desktop users should add the hosted connector from the Connectors Directory instead: nothing to download, no token to paste, and it also works on claude.ai and mobile. Use the extension below when you want local card audio, which only a local server can provide.
One-click extension (for card audio)
- Download
engramo.mcpbfrom the latest release (macOS on Intel and Apple Silicon, Windows x64, Linux x64 and arm64). - Double-click it (or drag it onto Claude Desktop) and click Install. Claude Desktop shows a red warning
that the extension gets access to everything on your computer and that the developer isn't verified by
Anthropic. It shows this for every extension installed from a file — the Connectors Directory no longer lists
desktop extensions — and it's accurate: the extension runs the
engramo-mcpbinary with your user permissions. - Paste your EngrAmo API token, and optionally your Gemini API keys (comma-separated) to enable card audio. Claude Desktop stores both in the OS keychain.
No Node.js and no config file. Change the token or keys later under Settings → Extensions → EngrAmo →
Configure. The extension doesn't auto-update: install the new engramo.mcpb to upgrade. Its tools are
available in Chat conversations, not in Claude Desktop's Code mode. The bundle is built from
mcpb/manifest.json by the release workflow. Verify a download with
gh attestation verify engramo.mcpb -R engramo-developer/engramo-mcp --signer-workflow engramo-developer/engramo-mcp/.github/workflows/release.yml --source-ref refs/tags/vX.Y.Z
(substituting the release tag), or compare it with the release's
engramo.mcpb.sha256.
Manual config
Open Settings → Developer → Edit Config, or edit the file directly:
~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or
%APPDATA%\Claude\claude_desktop_config.json (Windows):
Using npx (no prior installation needed):
{
"mcpServers": {
"engramo": {
"command": "npx",
"args": ["-y", "@engramo/mcp"],
"env": {
"ENGRAMO_API_URL": "https://api.engramo.app",
"ENGRAMO_API_TOKEN": "your-token",
"ENGRAMO_TTS_GEMINI_API_KEYS": "your-gemini-key"
}
}
}
}
ENGRAMO_TTS_GEMINI_API_KEYS is optional — it enables list_tts_voices/generate_card_audio using your own
Gemini quota; omit the line to disable. See Text-to-speech.
Using a global install (npm install -g @engramo/mcp):
{
"mcpServers": {
"engramo": {
"command": "engramo-mcp",
"env": {
"ENGRAMO_API_URL": "https://api.engramo.app",
"ENGRAMO_API_TOKEN": "your-token"
}
}
}
}
After editing, fully quit Claude Desktop (⌘Q on macOS — closing the window isn't enough) and reopen it.
If the server doesn't show up, check the logs at ~/Library/Logs/Claude/mcp-server-engramo.log (macOS) or
%APPDATA%\Claude\logs\ (Windows). The Gemini key is never logged.
Claude (hosted connector)
Use the hosted server from claude.ai, Claude Desktop, or Claude mobile — no install, no token:
- Open EngrAmo in the Connectors Directory (or Settings → Connectors → Browse and search for EngrAmo).
- Connect, sign in with your EngrAmo account, and approve.
It's a community connector: Anthropic ran automated reviews but hasn't verified it, so Claude shows a short notice
before you connect. Without the directory — for example, if your organization restricts it — add it by URL
instead: Settings → Connectors → Add custom connector, paste https://mcp.engramo.app/, then Connect.
All hosted tools are then available. Like every remote connection, it has no local text-to-speech; use the Claude Desktop extension for that.
Claude Code
Register the server once for all your projects (--scope user):
claude mcp add engramo --scope user \
-e ENGRAMO_API_URL=https://api.engramo.app \
-e ENGRAMO_API_TOKEN=your-token \
-e ENGRAMO_TTS_GEMINI_API_KEYS=your-gemini-key \
-- npx -y @engramo/mcp
ENGRAMO_TTS_GEMINI_API_KEYS is optional — it enables list_tts_voices/generate_card_audio using your own
Gemini quota; omit the -e ENGRAMO_TTS_GEMINI_API_KEYS=… line to disable. See
Text-to-speech.
With a global install, replace npx -y @engramo/mcp with engramo-mcp.
Codex
The OpenAI Codex CLI and the Codex IDE extension share one config file, ~/.codex/config.toml. This is the
way to use your ChatGPT account's models with local TTS (the ChatGPT app's remote connector can't have it):
Using npx (no prior installation needed):
[mcp_servers.engramo]
command = "npx"
args = ["-y", "@engramo/mcp"]
tool_timeout_sec = 300 # default 60s; a 20-card generate_card_audio batch can take longer
env = { ENGRAMO_API_URL = "https://api.engramo.app", ENGRAMO_API_TOKEN = "your-token", ENGRAMO_TTS_GEMINI_API_KEYS = "your-gemini-key" }
ENGRAMO_TTS_GEMINI_API_KEYS is optional — it enables list_tts_voices/generate_card_audio using your own
Gemini quota; omit it from env to disable. See Text-to-speech.
Using a global install (npm install -g @engramo/mcp):
[mcp_servers.engramo]
command = "engramo-mcp"
env = { ENGRAMO_API_URL = "https://api.engramo.app", ENGRAMO_API_TOKEN = "your-token" }
VS Code (GitHub Copilot agent mode)
Add to .vscode/mcp.json (workspace), or run MCP: Open User Configuration from the Command Palette for a
user-wide config. Use inputs with "password": true so VS Code prompts for the secrets and stores them
itself instead of you writing them into the file. Note the top-level key is servers (not mcpServers):
Using npx (no prior installation needed):
{
"inputs": [
{ "type": "promptString", "id": "engramo-token", "description": "EngrAmo API token", "password": true },
{ "type": "promptString", "id": "gemini-key", "description": "Gemini API key (for TTS)", "password": true }
],
"servers": {
"engramo": {
"type": "stdio",
"command": "npx",
"args": ["-y", "@engramo/mcp"],
"env": {
"ENGRAMO_API_URL": "https://api.engramo.app",
"ENGRAMO_API_TOKEN": "${input:engramo-token}",
"ENGRAMO_TTS_GEMINI_API_KEYS": "${input:gemini-key}"
}
}
}
}
ENGRAMO_TTS_GEMINI_API_KEYS is optional — it enables list_tts_voices/generate_card_audio using your own
Gemini quota; omit the line (and the gemini-key input) to disable. See
Text-to-speech.
With a global install, use "command": "engramo-mcp" and drop args.
Cursor
Add to ~/.cursor/mcp.json:
Using npx (no prior installation needed):
{
"mcpServers": {
"engramo": {
"command": "npx",
"args": ["-y", "@engramo/mcp"],
"env": {
"ENGRAMO_API_URL": "https://api.engramo.app",
"ENGRAMO_API_TOKEN": "your-token",
"ENGRAMO_TTS_GEMINI_API_KEYS": "your-gemini-key"
}
}
}
}
ENGRAMO_TTS_GEMINI_API_KEYS is optional — it enables list_tts_voices/generate_card_audio using your own
Gemini quota; omit the line to disable. See Text-to-speech.
Using a global install (npm install -g @engramo/mcp):
{
"mcpServers": {
"engramo": {
"command": "engramo-mcp",
"env": {
"ENGRAMO_API_URL": "https://api.engramo.app",
"ENGRAMO_API_TOKEN": "your-token"
}
}
}
}
Windsurf
Add to ~/.codeium/windsurf/mcp_config.json:
Using npx (no prior installation needed):
{
"mcpServers": {
"engramo": {
"command": "npx",
"args": ["-y", "@engramo/mcp"],
"env": {
"ENGRAMO_API_URL": "https://api.engramo.app",
"ENGRAMO_API_TOKEN": "your-token",
"ENGRAMO_TTS_GEMINI_API_KEYS": "your-gemini-key"
}
}
}
}
ENGRAMO_TTS_GEMINI_API_KEYS is optional — it enables list_tts_voices/generate_card_audio using your own
Gemini quota; omit the line to disable. See Text-to-speech.
Using a global install (npm install -g @engramo/mcp):
{
"mcpServers": {
"engramo": {
"command": "engramo-mcp",
"env": {
"ENGRAMO_API_URL": "https://api.engramo.app",
"ENGRAMO_API_TOKEN": "your-token"
}
}
}
}
Gemini CLI
Add to ~/.gemini/settings.json:
Using npx (no prior installation needed):
{
"mcpServers": {
"engramo": {
"command": "npx",
"args": ["-y", "@engramo/mcp"],
"env": {
"ENGRAMO_API_URL": "https://api.engramo.app",
"ENGRAMO_API_TOKEN": "your-token",
"ENGRAMO_TTS_GEMINI_API_KEYS": "your-gemini-key"
}
}
}
}
ENGRAMO_TTS_GEMINI_API_KEYS is optional — it enables list_tts_voices/generate_card_audio using your own
Gemini quota; omit the line to disable. See Text-to-speech.
Using a global install (npm install -g @engramo/mcp):
{
"mcpServers": {
"engramo": {
"command": "engramo-mcp",
"env": {
"ENGRAMO_API_URL": "https://api.engramo.app",
"ENGRAMO_API_TOKEN": "your-token"
}
}
}
}
Antigravity
Antigravity supports both transports directly via its own config file — no separate remote-connector UI
needed like ChatGPT's. Add to ~/.gemini/config/mcp_config.json (global) or .agents/mcp_config.json
(workspace-local):
stdio, via npx (no prior installation needed):
{
"mcpServers": {
"engramo": {
"command": "npx",
"args": ["-y", "@engramo/mcp"],
"env": {
"ENGRAMO_API_URL": "https://api.engramo.app",
"ENGRAMO_API_TOKEN": "your-token"
}
}
}
}
Remote, over Streamable HTTP (point at a running engramo-mcp http deployment — see below).
engramo-mcp supports OAuth 2.1 dynamic client registration, so Antigravity can handle
authentication automatically — no token to paste or manage:
{
"mcpServers": {
"engramo": {
"serverUrl": "https://mcp.engramo.app/"
}
}
}
If you'd rather use a static token instead (no OAuth login prompt), add headers manually:
{
"mcpServers": {
"engramo": {
"serverUrl": "https://mcp.engramo.app/",
"headers": {
"Authorization": "Bearer your-token"
}
}
}
}
Text-to-speech is unavailable over the remote transport — use the stdio config above if you want
generate_card_audio.
Use in ChatGPT (remote MCP over Streamable HTTP)
engramo-mcp also runs as a remote server, so it can be added to ChatGPT as a custom connector (developer
mode) without installing anything locally. The hosted server at https://mcp.engramo.app/ already works this way. This is the same binary — the http subcommand instead of the
default stdio — so a self-hosted deployment serves both Claude Desktop users (stdio) and ChatGPT users
(HTTP) from one codebase.
ENGRAMO_API_URL=https://api.engramo.app MCP_BIND_ADDR=0.0.0.0:8080 engramo-mcp http
This serves MCP over Streamable HTTP at POST / (root). Unlike stdio mode, there is no global
ENGRAMO_API_TOKEN — every session authenticates with its own Authorization: Bearer <token> header, so one
deployment safely serves many users at once (each session's calls to the EngrAmo API use only that session's
token). Requests without a well-formed, non-empty bearer token are rejected with 401 before a session is
created, and once a session exists every later request on it must carry the same token that opened it.
http mode also serves an unauthenticated GET /version that returns {"name","version"} (this
binary's build version, read from Cargo.toml at compile time) — handy for deployment health and
version probes without an MCP handshake.
In ChatGPT, enable developer mode (Settings → Connectors → Advanced → Developer mode), then add an app
pointing at https://mcp.engramo.app/ (or your own deployment's https://<host>/). ChatGPT signs you in
through OAuth: sign in with your EngrAmo account and approve — no API token needs to be pasted. (Clients
without OAuth can instead send an EngrAmo API token from Settings → API Tokens as a static
Authorization: Bearer header.) Once connected, prompts like "Make me a 10-card Spanish restaurant deck" or "Turn this
conversation into flashcards" call generate_catalog_with_cards directly; open the result at
https://study.engramo.app/catalog/<shortId> to study it.
generate_card_audiois never available through a ChatGPT connector — it's a remote connection, and local text-to-speech isstdio-only by design. To use your ChatGPT account's models with local TTS, use Codex instead.
A listing in the public ChatGPT app directory is coming; until then, add the app in developer mode as above.
ChatGPT plugin package (maintainers)
The directory listing is submitted on platform.openai.com/plugins as a
plugin ZIP built from chatgpt-plugin/: plugin.json holds the listing metadata and
mcp.json points at https://mcp.engramo.app/ with OAuth via dynamic client registration (no client ID or
secret). Build it with:
./chatgpt-plugin/build.sh # → target/engramo-chatgpt-plugin.zip
The script copies the icon from mcpb/ and sets version from Cargo.toml. Upload a new ZIP (Plugins →
EngrAmo → Upload new version) only when the listing metadata changes; tool and server changes are picked up
by Rescan. The domain-verification token served at /.well-known/openai-apps-challenge comes from the
portal and is set as the _OPENAI_APPS_CHALLENGE substitution on the prod Cloud Build trigger, not in this repo.
Bring-your-own-AI
Every deployment is bring-your-own-AI: the calling model (Claude, ChatGPT, …) does all generation —
translation, dictionaries, phrasing — and engramo-mcp only persists the result via generate_card /
generate_catalog_with_cards / generate_cards. This costs the EngrAmo account nothing beyond normal
storage quotas, and needs no paid EngrAmo plan.
Environment variables
| Variable | Default | Description |
|---|---|---|
ENGRAMO_API_URL | https://api.engramo.app | Base URL of the EngrAmo API |
ENGRAMO_API_TOKEN | (required for stdio) | Your EngrAmo API token. Unused in http mode — each session supplies its own via the Authorization: Bearer header. |
MCP_BIND_ADDR | 0.0.0.0:8080 | Bind address for http mode |
MCP_PUBLIC_URL | (unset) | http mode only — this deployment's own public URL (bare origin, no path — the endpoint is served at /), e.g. https://mcp.engramo.app. Also allowlists that host for inbound requests; without it, every request is rejected (see MCP_ALLOWED_HOSTS). |
MCP_ALLOWED_HOSTS | (unset) | http mode only — extra comma-separated hostnames/host:port values to permit, on top of the one derived from MCP_PUBLIC_URL. Only needed for extra entry points (e.g. a Cloud Run service's own *.run.app fallback URL alongside its custom domain). |
ENGRAMO_TTS_GEMINI_API_KEYS | (unset → TTS tools absent) | stdio only. Your own Gemini API key, or a comma-separated list. Enables list_tts_voices/generate_card_audio. Ignored (with a startup warning) in http mode — see Text-to-speech below. |
ENGRAMO_TTS_PROVIDER | gemini | stdio only. TTS engine selector. Only gemini is supported today; an unrecognized value fails startup. |
ENGRAMO_TTS_MODEL | gemini-3.8-flash-tts | stdio only. Gemini TTS model id. |
ENGRAMO_TTS_VOICE | Puck | stdio only. Default voice used when a generate_card_audio call doesn't specify one. |
Available tools
34 tools are always available, and two more appear with text-to-speech
(local stdio only).
For ready-to-paste prompts that use them, see docs/prompt-examples.md.
Server info
| Tool | Description |
|---|---|
get_server_version | Get this MCP server's own build version (not a catalog/card's version field). No API call |
get_subscription_usage | Your userId, plan and features, and every limit: used, remaining, unlimited or not included, its window (lifetime or monthly, plus daily/hourly sub-limits) and when it resets (UTC). Call it after a "Quota exceeded" or "Not included in your plan" error |
Catalogs
| Tool | Description |
|---|---|
list_catalogs | List your catalogs with id, name, description, tags, card count, visibility and version; filter (own or shared) limits it to catalogs you own or that others shared with you, and speaking_only to catalogs with shadowing on |
get_catalog | Get a single catalog by ID, including its description, tags and shadowing settings |
update_catalog | Update a catalog's name, description, tags, visibility or cover image (image_id, from upload_media); needs the current version |
set_catalog_shadowing | Turn shadowing (speaking practice) on or off for a catalog, or change its language |
share_catalog | Share a catalog you own with other users by user id (UUID, not email) as viewer (default) or editor; re-sharing changes their level. Returns the full collaborator list. Ask people for their user id; they can get it from get_subscription_usage |
unshare_catalog | Remove users' access to a catalog you own. Returns the remaining collaborators |
list_catalog_shares | List who a catalog is shared with and their access level (owner or editors) |
delete_catalog | Delete a catalog. It is archived instead if it has cards, a cover image, or is in an active learning path; there is no restore |
Cards
| Tool | Description |
|---|---|
list_cards | List cards in a catalog |
get_card | Get a single card by ID, with its face, back and catalog memberships |
update_card | Update a card's face, back or catalog memberships (needs the current version) |
delete_card | Delete a card; a card with learning progress is archived instead |
Learning
| Tool | Description |
|---|---|
get_due_cards | Get cards due for review today, sorted by priority |
get_all_learning_cards | Get all cards in the learning queue (due and future) |
add_card_to_learning | Add a card to the learning queue |
add_catalog_to_learning | Add all cards from a catalog to the learning queue |
Reviews are graded in the EngrAmo app; there is no tool for submitting a review.
Learning paths
| Tool | Description |
|---|---|
list_learning_paths | List all learning paths |
get_learning_path | Get a single learning path by ID, including its catalogs |
create_learning_path | Create a learning path, optionally adding catalogs right away (catalog_ids) |
update_learning_path | Update a path's name, description, tags or visibility (public or private; needs the current version) |
add_catalog_to_learning_path | Add a catalog to a learning path |
remove_catalog_from_learning_path | Remove a catalog from a learning path |
activate_learning_path | Activate a learning path so its catalogs are included in daily reviews |
deactivate_learning_path | Deactivate a learning path so its catalogs are left out of daily reviews |
Search
| Tool | Description |
|---|---|
search_global | Search across cards, catalogs and (on newer API versions) learning paths |
search_catalogs | Search catalogs by name, description or short ID (the ~8-character code shown in the app) |
search_learning_paths | Search learning paths by name or description |
Media
| Tool | Description |
|---|---|
list_media | List the files you uploaded, optionally filtered by type (image, audio, ...) |
upload_media | Upload a file you already have (a voice recording, an image) and get back a media_id to attach to a card or catalog |
Creating cards (bring-your-own-AI)
| Tool | Description |
|---|---|
generate_card | Create a flashcard — the calling model does any translation/wording itself |
generate_catalog_with_cards | Create a catalog with cards in one call, optionally with a cover image (image_id) — same bring-your-own-AI model. To add or replace the cover of an existing catalog, use update_catalog |
generate_cards | Add multiple cards to an existing catalog — same bring-your-own-AI model |
Text-to-speech (stdio, opt-in)
Only present when ENGRAMO_TTS_GEMINI_API_KEYS is set (stdio mode only) — see
Text-to-speech below.
| Tool | Description |
|---|---|
list_tts_voices | List the configured TTS engine, model, default voice, and available voices. No network call. |
generate_card_audio | Synthesize speech for the FACE side of up to 20 cards, upload it to your own media, and attach it — using your own Gemini key/quota |
Rich cards: dictionary, styling, images, audio
Beyond plain text, a card's face/back can carry:
| Field | What it does |
|---|---|
dictionary | Word → translation map, rendered as clickable highlights in the app |
rich_text | Styled spans (bold, color, monospace) within the text |
style | Font/color/background/alignment for the whole face or back |
audio_id | Your own audio (a recording, a clip from your own TTS) — upload it with upload_media first, or set it automatically by calling generate_card_audio (stdio only, see below) |
visual_id + visual_type | Your own image/video, same pattern as audio_id |
A catalog can also have image_id (cover image), set with generate_catalog_with_cards or later with update_catalog (a cover can be replaced, not removed). None of this needs a paid EngrAmo
plan — dictionary/translation are done by the calling model, images are entirely bring-your-own via
upload_media (max ~10MB per file), and face audio is either bring-your-own the same way or
generated locally with your own TTS key via generate_card_audio. See engramo://card-schema for
the full schema and four worked examples, and docs/prompt-examples.md
for ready-to-paste prompts covering all of this end to end.
Adding a lot of cards/media at once? upload_media is one file per call, fine for a handful of
files through a chat client. For bulk imports (many cards, many audio files, in one request) see
docs/bulk-import.md — a direct API recipe for CLI/scripted use (Claude
Code, a terminal, a script), not a chat-driven MCP tool.
Text-to-speech (bring your own key)
Set ENGRAMO_TTS_GEMINI_API_KEYS and two extra tools appear: list_tts_voices and
generate_card_audio. Given a list of card ids, generate_card_audio synthesizes speech for each
card's face text with Gemini TTS, encodes it to MP3, uploads it to your own EngrAmo media, and
attaches it as that card's audio_id — the same place a manually recorded upload_media file would
go, just done for you.
stdio only, and on purpose. The key is read once, locally, from your own machine's
environment and is never sent to EngrAmo or logged. In http mode the server is a shared,
multi-tenant deployment — there is no per-session place to put a secret key that stays on the
caller's machine — so ENGRAMO_TTS_GEMINI_API_KEYS is read only by stdio; in http mode it is
ignored entirely (with a startup warning naming the variable, never its value) and neither tool is
registered.
Why a namespaced variable instead of the standard GEMINI_API_KEY? GEMINI_API_KEY is the
de-facto standard name set by the Gemini CLI, Google's own SDKs, and many developers' shell
profiles. If engramo-mcp read it too, a key already sitting in your environment for an unrelated
tool could silently start spending itself the moment you ran engramo-mcp stdio. engramo-mcp
never reads GEMINI_API_KEY — only the explicit, namespaced ENGRAMO_TTS_GEMINI_API_KEYS opts
you in.
Whose quota does this spend? Yours, not EngrAmo's: generate_card_audio spends only your own
Gemini API quota/billing, same as if you'd called Gemini directly.
Multiple keys / rotation. ENGRAMO_TTS_GEMINI_API_KEYS accepts a comma-separated list. On each
synthesis call the keys are tried in a random order; a key that's rate-limited, rejected (invalid or
revoked), or hits a transient error is skipped in favor of the next one, so one bad key in the list
doesn't fail the whole call.
Limits. Up to 20 cards per generate_card_audio call, up to 500 characters of face text per
card (longer cards are skipped, not truncated). Cards that already have face audio are skipped
unless you pass overwrite: true, in which case the previous audio is not deleted — it stays
in your EngrAmo media storage and keeps showing up in list_media. Call list_tts_voices first
to see the configured model and voice catalog.
Getting a key. Create one for free at Google AI Studio.
Where the key goes. Every stdio client section above shows the exact place for
ENGRAMO_TTS_GEMINI_API_KEYS — see Choosing a client. It's always one more entry
next to ENGRAMO_API_TOKEN in the server's env:
"env": {
"ENGRAMO_API_URL": "https://api.engramo.app",
"ENGRAMO_API_TOKEN": "your-token",
"ENGRAMO_TTS_GEMINI_API_KEYS": "your-gemini-key"
}
Quick check. Ask the assistant to call list_tts_voices. If the tool is missing, the key isn't set
(or the client wasn't restarted) or the client is using a remote connection.
Third-party licenses
The prebuilt binaries distributed via npm statically link LAME
(LGPL-2.0) for the MP3 encoding generate_card_audio uses. See
THIRD_PARTY_LICENSES for the full notice and source-availability terms.
Resources and Prompts
The server also exposes MCP Resources (live data readable as context):
| URI | Description |
|---|---|
engramo://card-schema | CardContent JSON schema with validation rules and examples |
engramo://catalogs | All catalogs (id, name, card_count) |
engramo://learning/due | Cards due for review today |
engramo://learning/stats | Learning stats (due_count, total_count) |
engramo://learning-paths | All learning paths (id, name) |
engramo://subscription | Your userId, plan, limits and usage (same view as get_subscription_usage) |
And MCP Prompts (guided workflows):
| Prompt | Description |
|---|---|
review_session | Start a guided spaced-repetition review session |
create_flashcard | Create a high-quality flashcard for a topic |
create_language_deck | Create a styled, translated, dictionary-annotated language-learning deck — see docs/prompt-examples.md |
explain_card | Explain a flashcard in depth with examples |
study_plan | Build a structured study plan from your catalogs |
Publishing to npm
git tag vx.y.z
git push origin vx.y.z
The release workflow also publishes server.json to the official
MCP Registry as app.engramo/mcp, after the GitHub release and npm
packages exist. One-time setup, key rotation and bumping the pinned mcp-publisher are in
docs/mcp-registry.md.
Contributing
Bug reports and pull requests are welcome at github.com/engramo-developer/engramo-mcp/issues.
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