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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
🚀 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:
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:
🔄 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 Approval: Wait for user approval of the completed task
- Repeat: Continue with the next task until all tasks are complete
- Final Approval: Get user approval for the entire request
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.
approve_task_completion
Approve a completed task.
approve_request_completion
Approve an entire request 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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Tools
A task management server that helps AI assistants break down user requests into manageable tasks and track their completion with user approval steps.
- 🌟 Overview
- ✨ Features
- 🚀 Installation
- 🛠️ Usage
- 🔄 Workflow
- 🧰 Available Tools
- 📚 Documentation
- 📝 License
- 🤝 Contributing
- 📜 Changelog
- 🙏 Acknowledgements
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