TaskFlow MCP 🔄✅
A task management Model Context Protocol (MCP) server for planning and executing tasks with AI assistants.
🌟 Overview
TaskFlow MCP is a specialized server that helps AI assistants break down user requests into manageable tasks and track their completion. It enforces a structured workflow with user approval steps to ensure tasks are properly tracked and users maintain control over the process.
✨ Features
- 📋 Task Planning: Break down complex requests into manageable tasks
- 🔍 Subtasks: Divide tasks into smaller, more manageable subtasks
- 📊 Progress Tracking: Track the status of tasks, subtasks, and requests with visual progress tables
- 👍 User Approval: Enforce user approval steps to ensure quality and control
- 💾 Persistence: Save tasks and requests to disk for persistence across sessions
- 🔄 Flexible Management: Add, update, or delete tasks and subtasks as needed
- 📝 Detailed Reporting: View task details and progress tables
- 📤 Export Options: Export task plans and status reports in Markdown, JSON, or HTML formats
- 📦 Dependencies: Track project and task-level dependencies with version information
- 📌 Notes: Add project-level notes for important information and preferences
- 📄 YAML Support: Save tasks in YAML format for better handling of multiline content
- 🛡️ Robust Text Handling: Comprehensive newline sanitization for reliable data persistence
🚀 Installation
Global Installation
Local Installation
🛠️ Usage
Starting the Server
If installed globally:
If installed locally:
Configuration
By default, TaskFlow MCP saves tasks to ~/Documents/tasks.json
. You can change this by setting the TASK_MANAGER_FILE_PATH
environment variable:
YAML Format Support
TaskFlow MCP supports both JSON and YAML formats for data persistence. To use YAML format, simply configure your file path with a .yaml
or .yml
extension:
YAML format is particularly useful for:
- Better preservation of multiline descriptions and text content
- More human-readable task data files
- Easier manual editing if needed
The format is automatically detected based on the file extension, and the system maintains full backward compatibility with existing JSON files.
MCP Configuration
To use TaskFlow MCP with AI assistants, you need to configure your MCP client to use the server. Create an mcp_config.json
file with the following content:
For YAML format:
🔄 Workflow
TaskFlow MCP enforces a specific workflow:
- Plan Tasks: Break down a user request into tasks (with optional subtasks)
- Get Next Task: Retrieve the next pending task
- Complete Subtasks: If the task has subtasks, complete each subtask before marking the task as done
- Mark Task Done: Mark a task as completed (requires all subtasks to be completed first)
- Wait for User Confirmation: Ask the user to confirm the completed task before proceeding
- Repeat: Continue with the next task until all tasks are complete
- Final Confirmation: Confirm with the user that the entire request has been completed
For AI assistants to consistently follow this workflow, see the example-system-prompt.md file for system prompts you can add to your assistant's instructions.
🧰 Available Tools
TaskFlow MCP exposes the following tools to AI assistants:
plan_task
Register a new user request and plan its associated tasks (with optional subtasks).
get_next_task
Retrieve the next pending task for a request.
mark_task_done
Mark a task as completed.
open_task_details
Get details about a specific task.
list_requests
List all requests in the system.
add_tasks_to_request
Add more tasks to an existing request.
update_task
Update a task's title or description.
delete_task
Delete a task from a request.
add_subtasks
Add subtasks to an existing task.
mark_subtask_done
Mark a subtask as completed.
update_subtask
Update a subtask's title or description.
delete_subtask
Delete a subtask from a task.
export_task_status
Export the current status of all tasks in a request to a file. It's recommended to use absolute paths for more reliable file creation.
add_note
Add a note to a request.
update_note
Update an existing note.
delete_note
Delete a note from a request.
add_dependency
Add a dependency to a request or task.
📚 Documentation
For more detailed information about the project architecture and implementation, see the OVERVIEW.md file.
📝 License
This project is licensed under the MIT License - see the LICENSE file for details.
🤝 Contributing
Contributions are welcome! Please see the CONTRIBUTING.md file for guidelines.
📜 Changelog
See the CHANGELOG.md file for a history of changes to this project.
🙏 Acknowledgements
- Built with Model Context Protocol (MCP)
- Created by Pink Pixel
Made with ❤️ by Pink Pixel
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