MCP 服务器

flashcard-mcp

A lightweight MCP server that adds spaced-repetition flashcards (SM-2) to AI assistants, storing data locally in SQLite and enabling card creation, review, and scheduling.

README

🃏 Flashcard MCP

Give any AI assistant a memory that actually lasts.

A lightweight MCP server that adds spaced-repetition flashcards (the classic SM-2 algorithm behind SuperMemo and Anki) to Claude, Cursor, VS Code, and every other MCP-compatible assistant.

Stop re-teaching your AI the same thing every session. Your assistant now learns vocabulary, concepts, and facts once — then reviews them at the optimal moment so they stick forever.

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Why?

Every AI session starts from zero. You ask your assistant to help you learn English words, it makes a nice list — and by tomorrow both of you have forgotten everything.

Flashcard MCP fixes this: it stores flashcards locally and schedules reviews using the SM-2 algorithm, the scientifically-backed method that powers SuperMemo and Anki. The AI creates the cards, you review them, and the server decides exactly when to bring each card back so it moves from short-term to long-term memory.

🔒 100% local — your data lives in a single SQLite file. No cloud, no accounts, no tracking.
🧠 Real spaced repetition — the SM-2 algorithm, not a random "quiz".
⚡ Zero config — install, add two lines to your MCP config, done.
🐍 Pure Python, zero heavy dependencies — one tiny package, nothing else.

Installation

pip install flashcard-mcp

Configuration

Add the server to your MCP client. Examples for the most common setups:

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "flashcards": {
      "command": "flashcard-mcp",
      "args": []
    }
  }
}

VS Code / Cursor (.mcp.json):

{
  "servers": {
    "flashcards": {
      "type": "stdio",
      "command": "flashcard-mcp"
    }
  }
}

Data location: the database is created at ./data/flashcards.db next to the package. Override it with the FLASHCARD_DB environment variable.

How it works

You ask your AI to build a deck — e.g. "Create a deck 'English' and add cards for these phrasal verbs: look up, break down, give up."
The AI calls the tools — the server creates the deck and cards in SQLite.
When you want to review — ask "Let's review my due cards." The server returns exactly the cards that are due today, per the SM-2 schedule.
You grade each card (0–5), the algorithm updates the easiness factor and picks the next review date. Perfect recalls → longer intervals. Misses → the card comes back tomorrow.
Repeat — over time each card is reviewed at expanding intervals and permanently lands in your long-term memory.

Tools

Tool	Description
`create_deck`	Create a study deck
`list_decks`	List all decks with card counts
`delete_deck`	Delete a deck and its cards
`add_card`	Add a flashcard (front / back)
`list_cards`	List flashcards, filtered by deck
`delete_card`	Delete a flashcard
`get_due_cards`	Get cards due for review today
`review_card`	Grade a card (quality 0–5) and reschedule it
`get_stats`	Total cards, due today, per-deck breakdown
`export_cards`	Export everything as JSON

The SM-2 algorithm

When you grade a card, the server updates two numbers:

Easiness factor (EF) — how easy this card is for you. Starts at 2.5, never drops below 1.3.
Interval (days) — the gap until the next review. 1 → 6 → 6×EF → 6×EF² → …

A grade below 3 resets the card to a 1-day interval. This is exactly the schedule that makes spaced repetition effective — it targets reviews right before you'd forget.

Development

pip install -e ".[dev]"
pytest

Project structure

flashcard_mcp/
├── src/flashcard_mcp/
│   ├── srs.py      # SM-2 scheduling algorithm
│   ├── db.py       # SQLite storage layer
│   └── server.py   # MCP server + tool definitions
└── tests/          # unit + end-to-end stdio tests