FastMCP Task Manager Server

A comprehensive task management server built with FastMCP, ready for deployment on Railway.

Features

🛠️ Tools (Actions)

• create_task - Create new tasks with title, description, priority, due date, and tags
• get_task - Retrieve a specific task by ID
• update_task - Update task details
• delete_task - Delete a task
• list_tasks - List all tasks with optional filtering by status or tag
• complete_task - Mark a task as completed
• get_task_stats - Get task statistics and overview
• health_check - Server health status

📄 Resources (Data Access)

• task://{task_id} - Get detailed formatted information about a specific task
• tasks://all - Get a formatted summary of all tasks

💬 Prompts (Templates)

• task_planning_prompt - A helpful prompt for task planning and organization

Data Models

Task Status

• todo - Planned but not started
• in_progress - Currently being worked on
• completed - Finished
• cancelled - Cancelled

Priority Levels

• low - Nice to have, no rush
• medium - Normal priority
• high - Important, should be done soon
• urgent - Critical, needs immediate attention

Quick Start

Local Development

1. Install dependencies:

pip install -r requirements.txt

1. Run the server:

python main.py

The server will start on http://localhost:8000

Deploy to Railway

1. Create a new Railway project
2. Connect your GitHub repository
3. Railway will automatically detect the Python app and deploy it
4. The server will be available at your Railway app URL

Manual Railway Deployment

If you prefer manual deployment:

1. Install Railway CLI:

npm install -g @railway/cli

1. Login and deploy:

railway login
railway init
railway up

Environment Variables

• PORT - Server port (automatically set by Railway)
• PYTHON_VERSION - Python version (set to 3.11 in railway.toml)

API Examples

Creating a Task

# Using the FastMCP tool
create_task({
    "title": "Complete project documentation",
    "description": "Write comprehensive docs for the new feature",
    "priority": "high",
    "due_date": "2025-07-15T10:00:00Z",
    "tags": ["documentation", "project"]
})

Listing Tasks

# Get all tasks
list_tasks()

# Get only completed tasks
list_tasks(status="completed")

# Get tasks with specific tag
list_tasks(tag="urgent")

Getting Task Info

# Get task by ID
get_task("1")

# Get formatted task resource
# Access resource: task://1

Architecture

This FastMCP server uses:

• FastMCP Framework for MCP protocol handling
• Pydantic for data validation and serialization
• In-memory storage (replace with database for production)
• Type hints for automatic schema generation
• Decorator-based tool and resource definitions

Production Considerations

For production use, consider:

1. Database Integration: Replace in-memory storage with PostgreSQL, MongoDB, etc.
2. Authentication: Add user authentication and authorization
3. Persistence: Implement data persistence across server restarts
4. Monitoring: Add logging and monitoring
5. Rate Limiting: Implement rate limiting for API calls
6. Backup: Set up data backup strategies

Example Use Cases

1. Personal Task Management: Individual productivity tracking
2. Team Collaboration: Shared task lists and project management
3. AI Assistant Integration: Let AI assistants help manage your tasks
4. Workflow Automation: Integration with other tools and services
5. Project Planning: Break down projects into manageable tasks

Testing the Server

Once deployed, you can test the server using any MCP-compatible client or by making direct HTTP requests to the endpoints.

Health Check

curl https://your-railway-app.railway.app/health

MCP Client Integration

Configure your MCP client to connect to your Railway URL to access all tools, resources, and prompts.

Support

For FastMCP documentation and support:

• FastMCP Documentation
• Model Context Protocol
• Railway Documentation