The MCP TaskManager is a serverless task management system designed for AI assistants to handle complex multi-step workflows with built-in user approval mechanisms. With this server, you can:
Break down complex tasks into manageable sub-tasks using
request_planning
Track progress via
get_next_task
and progress tablesMark tasks as completed with
mark_task_done
Require user approval for completed tasks and entire requests
Inspect task details and list all requests
Add, update, or delete tasks within existing requests
Persistently store task data using Cloudflare KV
Interact through a RESTful API compliant with the Model Context Protocol
Support cross-origin requests (CORS) for web integration
MCP Task Manager
A Model Context Protocol (MCP) server for comprehensive task management, deployed as a Cloudflare Worker. This open-source project enables AI assistants to plan, track, and manage complex multi-step requests efficiently with persistent storage using Cloudflare KV.
🚀 Features
- Request Planning: Break down complex requests into manageable tasks
- Task Management: Create, update, delete, and track task progress
- Approval Workflow: Built-in approval system for task and request completion
- Progress Tracking: Visual progress tables and detailed task information
- Persistent Storage: Uses Cloudflare KV for reliable data persistence
- Serverless Architecture: Deployed as a Cloudflare Worker for global availability
- RESTful API: HTTP endpoints for easy integration with any application
- CORS Support: Cross-origin requests enabled for web applications
📦 Deployment
Prerequisites
- Cloudflare account (free tier works)
- Wrangler CLI installed
- Node.js 18+ and npm/pnpm/yarn
- Git for cloning the repository
Quick Start
- Clone and setup the repository
- Login to CloudflareThis will open your browser to authenticate with Cloudflare.
- Create KV namespaceCopy the namespace ID from the output.
- Update configuration
Edit
wrangler.toml
and replace the KV namespace ID: - Build and deploy
Your MCP Task Manager will be deployed and accessible at:
https://mcp-taskmanager.your-subdomain.workers.dev
Advanced Configuration
Custom Worker Name
To deploy with a custom name, update wrangler.toml
:
Environment Variables
For different environments (development, staging, production):
Deploy to specific environments:
🔧 Usage
API Endpoints
The deployed worker provides two main endpoints:
POST /list-tools
- Get available MCP toolsPOST /call-tool
- Execute MCP tool functions
Testing Your Deployment
After deployment, test your worker with curl:
Available Tools
📋 Core Task Management
request_planning
- Register a new user request and plan its associated tasksget_next_task
- Get the next pending task for a requestmark_task_done
- Mark a task as completed with optional detailsapprove_task_completion
- Approve a completed taskapprove_request_completion
- Approve the completion of an entire request
⚙️ Task Operations
add_tasks_to_request
- Add new tasks to an existing requestupdate_task
- Update task title or description (only for pending tasks)delete_task
- Remove a task from a requestopen_task_details
- Get detailed information about a specific task
📊 Information & Monitoring
list_requests
- List all requests with their current status and progress
Example API Calls
List Available Tools
Plan a New Request
📊 Data Model
Task Structure
Request Structure
Task Status Flow
Tasks can only be updated when in "Pending" status. Once marked as done or approved, they become read-only.
🛠️ Development
Local Development
Testing
Debugging
View real-time logs:
KV Data Management
🏗️ Architecture
The MCP Task Manager is built as a Cloudflare Worker with the following components:
Components
- TaskManagerServer Class: Core business logic for task management
- Worker Interface: HTTP endpoints for MCP protocol communication
- Cloudflare KV Storage: Persistent data storage for tasks and requests
- MCP Protocol: Standard Model Context Protocol for AI assistant integration
- CORS Support: Enables web application integration
Benefits
- Global Edge Deployment: Low latency worldwide via Cloudflare's network
- Serverless: No server management, automatic scaling
- Persistent Storage: Data survives across deployments
- Cost Effective: Cloudflare's generous free tier
- High Availability: Built-in redundancy and failover
📈 Monitoring and Logs
Cloudflare Dashboard
View logs and metrics in the Cloudflare Dashboard:
- Go to Cloudflare Dashboard
- Navigate to Workers & Pages
- Select your
mcp-taskmanager
worker - View logs, metrics, and analytics
Real-time Monitoring
Key Metrics to Monitor
- Request Volume: Number of API calls
- Response Times: Latency of operations
- Error Rates: Failed requests and their causes
- KV Operations: Storage read/write performance
- Memory Usage: Worker memory consumption
Troubleshooting Common Issues
Issue | Cause | Solution |
---|---|---|
500 Internal Server Error | KV namespace not found | Check KV namespace ID in wrangler.toml |
CORS errors | Missing headers | Verify CORS headers in worker.ts |
Task not found | Invalid task/request ID | Check ID format and existence |
Build failures | TypeScript errors | Run npm run build locally first |
🤝 Contributing
We welcome contributions! Here's how to get started:
Development Setup
- Fork the repository
- Clone your fork:
git clone https://github.com/your-username/mcp-taskmanager.git
- Create a feature branch:
git checkout -b feature/amazing-feature
- Install dependencies:
npm install
- Make your changes
- Test locally:
npx wrangler dev --local
- Build and test:
npm run build
Contribution Guidelines
- Follow TypeScript best practices
- Add tests for new features
- Update documentation for API changes
- Use conventional commit messages
- Ensure all tests pass before submitting
Pull Request Process
- Commit your changes:
git commit -m 'Add amazing feature'
- Push to your branch:
git push origin feature/amazing-feature
- Open a Pull Request with:
- Clear description of changes
- Screenshots/examples if applicable
- Reference to any related issues
Areas for Contribution
- 🐛 Bug fixes and improvements
- 📚 Documentation enhancements
- ✨ New MCP tools and features
- 🧪 Test coverage improvements
License
This project is licensed under the MIT License - see the LICENSE file for details.
💬 Support
Getting Help
- GitHub Issues: Report bugs or request features
- Discussions: Ask questions and share ideas
- Documentation: Check this README and inline code comments
Community Resources
- MCP Documentation: Model Context Protocol
- Cloudflare Workers Docs: Learn more about Workers
Reporting Issues
When reporting bugs, please include:
- Your Cloudflare Worker URL
- Steps to reproduce the issue
- Expected vs actual behavior
- Error messages or logs
- Browser/client information
🙏 Acknowledgments
- Built with the Model Context Protocol SDK
- Powered by Cloudflare Workers
- Designed for seamless AI assistant integration
- Inspired by the need for better task management in AI workflows
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
Made with ❤️ for the AI community
Deploy your own instance and start managing tasks efficiently with AI assistants!
local-only server
The server can only run on the client's local machine because it depends on local resources.
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