FlashCardsMCP

This is a dockerized Python Model Context Protocol (MCP) server for managing flash card projects. It uses OpenAI embeddings and SQLite for semantic search and storage.

Features

• List all project names and ids

• Semantic search for project by name (using OpenAI embeddings)

• Get random flash card by project id

• Add flash card to project (with question, answer, optional hint, optional description)

• List all flash cards by project

• Semantic search for flash cards by query (using OpenAI embeddings)

• Global semantic search for cards across all projects

• Retrieve a card by its id

• All API/tool responses include a type field: project or card

• No binary embedding data is ever returned in API responses

Related MCP server: MCP-AnkiConnect

API/Tool Design

• All tools raise ValueError for not found or empty results

• Project and card creation tools return the full object, not just the id

• See .github/copilot-instructions.md for code generation rules

Getting Started

1. Install dependencies:

   pip install -r requirements.txt

2. Run the server:

   python main.py

3. Run with Docker:

   docker build -t flash-card-mcp . # Run with database persistence (recommended): docker run -v $(pwd)/storage:/app/storage/database.db flash-card-mcp

Environment Variables

• OPENAI_API_KEY: Required. Set this environment variable to your OpenAI API key to enable embedding generation. Example:

  export OPENAI_API_KEY=sk-...your-key...

  You must set this variable before running the server or running the Docker container.

Usage

This server exposes its API via the Model Context Protocol (MCP) using FastMCP. You can call the following tools:

• get_all_projects() → List all projects

• add_project(name) → Create a new project (returns full project dict)

• search_project_by_name(name) → Semantic search for a project (returns full project dict)

• get_random_card_by_project(project_id) → Get a random card from a project

• add_card(project_id, question, answer, hint=None, description=None) → Add a card (returns full card dict)

• get_all_cards_by_project(project_id) → List all cards in a project

• search_cards_by_embedding(project_id, query) → Semantic search for cards in a project

• global_search_cards_by_embedding(query) → Semantic search for cards across all projects

• get_card_by_id(card_id) → Retrieve a card by its id

All returned objects include a type field and never include binary embedding data.

Development

• All project and card data is stored in SQLite (database.db)

• Embeddings are generated using OpenAI's text-embedding-ada-002 model

• The server is implemented in main.py and db.py

• See .github/copilot-instructions.md for code and API rules

Inspector

For more details, see the code and docstrings in main.py and db.py.