MCP ToDo Server

Production-ready Model Context Protocol server for task and todo management with dual storage backends, error handling, and modern Python tooling.

Features

• Dual Storage: File-based JSON or database (SQLite/PostgreSQL)
• Todo Management: Standalone todos and structured tasks with priorities/due dates
• Bulk Operations: Efficient multi-item management and analytics
• Security: Input validation, error handling, containerization
• Modern Tooling: uv package manager, comprehensive testing, structured logging

Architecture

MCP Server → Business Logic → Storage (File/Database) → Infrastructure

Quick Start

# Clone and install
git clone https://github.com/AnoRebel/mcp-todo-server.git
cd mcp-todo-server
uv sync  # or pip install -r requirements.txt

# Run with file storage (default)
uv run python server.py

# Run with database storage
export MCP_STORAGE_TYPE=database
export MCP_DATABASE_URL=sqlite:///todos.db
uv run python server.py

# Docker
docker-compose up -d

# Development
make test && make format

Configuration

Environment variables:

• MCP_STORAGE_TYPE: file or database (default: file)
• MCP_DATABASE_URL: Database connection string
• MCP_LOG_LEVEL: DEBUG, INFO, WARNING, ERROR
• MCP_MAX_TASKS: Maximum tasks (default: 1000)
• MCP_MAX_TODOS: Maximum todos (default: 1000)

Usage

Basic Operations

# Standalone todos
add_todo("Buy groceries")
list_todos(status="open")
complete_todo("todo-id")

# Structured tasks
create_task(name="Project", priority="high", due_date="2024-12-31")
add_task_todo("task-id", "Subtask description")
bulk_task_operation(operation="complete", filters={"priority": "low"})

# Analytics
get_productivity_stats()

Error Handling

• Circuit breaker pattern for failure prevention
• Automatic retry with exponential backoff
• Categorized errors with user-friendly messages
• Comprehensive logging and monitoring

Testing

make test              # All tests with coverage
pytest tests/ -v       # Verbose test output

Coverage includes storage backends, CRUD operations, error handling, and data models.

Security & Performance

• Input validation and sanitization
• Container security (non-root execution)
• Database connection pooling and atomic operations
• Configurable limits and graceful degradation
• Structured logging for observability

Deployment

Docker

docker-compose up -d                              # File storage
docker-compose --profile database up -d           # With PostgreSQL

Kubernetes

apiVersion: apps/v1
kind: Deployment
metadata:
  name: mcp-todo-server
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: mcp-todo-server
        image: mcp-todo-server:latest
        env:
        - name: MCP_STORAGE_TYPE
          value: "database"
        - name: MCP_DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: db-secret
              key: url

Integration

Claude Desktop

{
  "mcpServers": {
    "todo-manager": {
      "command": "python",
      "args": ["/path/to/server.py"],
      "env": {
        "MCP_STORAGE_TYPE": "file",
        "MCP_DATA_DIR": "/Users/username/.claude_todos"
      }
    }
  }
}

Project Structure

mcp-todo-server/
├── server.py             # Main server
├── config.py            # Configuration
├── models/              # Data models
├── storage/             # Storage backends
├── tests/              # Test suite
├── docker-compose.yml  # Container setup
└── requirements.txt    # Dependencies

Contributing

1. Fork and create feature branch
2. Add tests and follow PEP 8
3. Run make test && make format
4. Submit PR with clear description

Performance

Storage Comparison:

• File: Simple, <1000 items recommended
• Database: Scalable, concurrent access, millions of items

Benchmarks:

Operation	File	SQLite	PostgreSQL
Add Todo	5ms	2ms	3ms
List 100	15ms	5ms	8ms

License

MIT License - see LICENSE file.

Support

• GitHub Issues for bugs/features
• Email: hacker4rebel@gmail.com

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Built with ❤️ following MCP best practices