WaniKani MCP Server

A production-ready MCP (Model Context Protocol) server that connects AI assistants like Claude Code to WaniKani learning data. Get real-time progress updates, identify problem items, and manage your Japanese learning workflow through natural conversation.

✨ Quick Start

For Users with Claude Code

1. Set up the MCP server connection:

   cd wanikani-mcp
   claude mcp add-json wanikani '{"command": "uv", "args": ["run", "python", "-m", "wanikani_mcp.server"], "env": {}, "cwd": "/path/to/wanikani-mcp"}'
   

2. Verify connection: Run claude mcp list - wanikani should show as "connected"
3. Register your WaniKani account:

   Register me with WaniKani API token: your-wanikani-token
   

4. Start learning:

   How many lessons and reviews do I have?
   What are my worst leeches that need practice?
   

For Developers

git clone https://github.com/jackedney/wanikani-mcp.git
cd wanikani-mcp
uv install
cp .env.example .env  # Configure your database
uv run alembic upgrade head
task dev

🎯 What This Does

Real Learning Integration: Your AI assistant can now see your actual WaniKani progress and help with personalized study strategies.

Architecture Overview

Core Features:

• Live Status: Current level, lesson count, review count, next review time
• Leech Detection: Identifies consistently difficult items for focused practice
• Background Sync: Keeps data fresh automatically (every 30 minutes)
• Multi-User: Secure per-user authentication with MCP API keys

Example Conversations:

You: "How am I doing with my Japanese studies?"
Claude: "You're Level 8 with 30 lessons and 206 reviews available. Your next reviews are at 11:00 PM."

You: "What should I focus on?"
Claude: "You have 15 leeches - items you consistently get wrong. Let's practice these kanji: 体, 茶, 牛..."

🔧 MCP Tools & Resources

Tools (Actions)

• register_user: Connect your WaniKani account securely
• get_status: Current level, lessons, reviews, next review time
• get_leeches: Items that need extra practice (wrong >3 times)
• sync_data: Manual data refresh from WaniKani

Resources (Data Access)

• user_progress: Detailed statistics and learning metrics
• review_forecast: Timeline of upcoming review sessions
• item_database: Searchable collection of your WaniKani items

🚀 Deployment Options

Render (Recommended)

1. Fork this repository
2. Connect to Render:
   • Create new Web Service from your fork
   • Environment: DATABASE_URL (PostgreSQL connection string)
   • Build: uv install
   • Start: uv run python -m wanikani_mcp.http_server
3. Run migrations: uv run alembic upgrade head

Local Development

# Install dependencies
uv install

# Set up database
cp .env.example .env
# Edit .env with your PostgreSQL URL
uv run alembic upgrade head

# Run in stdio mode (for Claude Code)
task dev

# Run in HTTP mode (for web access)  
task dev-http

Container (Podman/Docker)

# Build
podman build -t wanikani-mcp .

# Run
podman run -p 8000:8000 \
  -e DATABASE_URL="postgresql://user:pass@host/db" \
  wanikani-mcp

🔐 Security & Authentication

Two-Tier Security:

1. WaniKani API Token: Your personal WaniKani API key (from wanikani.com/settings/personal_access_tokens)
2. MCP API Key: Generated server key for Claude Code authentication

Registration Flow:

1. Get your WaniKani API token from https://www.wanikani.com/settings/personal_access_tokens
2. Use register_user tool with your WaniKani token
3. Receive MCP API key for all future requests
4. MCP API key authenticates you for get_status, get_leeches, sync_data

Privacy: Each user's data is completely isolated. MCP API keys are securely generated and hashed.

⚙️ Configuration

Environment Variables (copy .env.example to .env):

DATABASE_URL=postgresql://user:pass@host:5432/dbname
WANIKANI_API_BASE_URL=https://api.wanikani.com/v2
SYNC_INTERVAL_MINUTES=30
MAX_CONCURRENT_SYNCS=3
RATE_LIMIT_PER_MINUTE=60
LOG_LEVEL=INFO

MCP Server Configuration (for Claude Code):

Method 1: JSON Configuration (Recommended)

cd /path/to/wanikani-mcp
claude mcp add-json wanikani '{"command": "uv", "args": ["run", "python", "-m", "wanikani_mcp.server"], "env": {}, "cwd": "/path/to/wanikani-mcp"}'

Method 2: Manual Configuration

{
  "mcpServers": {
    "wanikani": {
      "command": "uv",
      "args": ["run", "python", "-m", "wanikani_mcp.server"],
      "cwd": "/path/to/wanikani-mcp",
      "env": {}
    }
  }
}

Important:

• Must specify cwd (working directory) for the server to find dependencies
• Server uses file logging to avoid interfering with stdio MCP communication
• Run uv install first to ensure dependencies are available

🧑‍💻 Development

Code Quality:

uv run ruff check .      # Linting
uv run ruff format .     # Formatting  
uvx ty check .           # Type checking
uv run pytest           # Testing

Database Management:

# Create migration
uv run alembic revision --autogenerate -m "description"

# Apply migrations
uv run alembic upgrade head

# Reset database (development only)
uv run python -c "from wanikani_mcp.database import reset_database; reset_database()"

Tech Stack:

• Framework: FastAPI + Python MCP SDK
• Database: PostgreSQL + SQLModel ORM
• Background Jobs: APScheduler
• Package Manager: uv
• Containerization: Podman
• Deployment: Render

🤝 Production Checklist

Before deploying:

• PostgreSQL database configured
• Environment variables set
• Database migrations applied
• Rate limiting configured
• Monitoring/logging enabled
• SSL/TLS certificates in place
• Backup strategy implemented

📈 Monitoring

Health Check Endpoint: GET /health Metrics: Built-in logging for sync operations, API calls, and errors Database: Monitor connection pool, query performance, storage usage

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📄 License

See LICENSE for details.

🙏 Acknowledgments

• WaniKani: For providing the excellent Japanese learning platform and API
• Anthropic: For the MCP specification and Claude Code integration
• Python MCP SDK: For the foundational MCP implementation