A task management Model Context Protocol (MCP) server that helps AI assistants break down user requests into manageable tasks with subtasks, dependencies, and notes. Enforces a structured workflow with user approval steps.
Overview
what is TaskFlow MCP?
TaskFlow MCP is a task management Model Context Protocol (MCP) server designed to assist AI assistants in planning and executing tasks by breaking down user requests into manageable components.
how to use TaskFlow MCP?
To use TaskFlow MCP, install it globally or locally via npm, start the server, and configure your MCP client to connect to it. You can then plan tasks, track progress, and manage approvals through various commands.
key features of TaskFlow MCP?
- Task Planning: Break down complex requests into manageable tasks.
- Subtasks: Divide tasks into smaller, manageable subtasks.
- Progress Tracking: Visualize task status and progress.
- User Approval: Enforce approval steps for quality control.
- Persistence: Save tasks for continuity across sessions.
- Export Options: Export task plans in various formats.
use cases of TaskFlow MCP?
- Managing project tasks for AI-driven applications.
- Coordinating team workflows with user approval processes.
- Tracking progress of complex user requests in AI systems.
FAQ from TaskFlow MCP?
- Can TaskFlow MCP handle multiple requests simultaneously?
Yes! It is designed to manage multiple tasks and requests efficiently.
- Is TaskFlow MCP free to use?
Yes! TaskFlow MCP is open-source and free to use under the MIT License.
- How can I contribute to TaskFlow MCP?
Contributions are welcome! Please refer to the contributing guidelines in the repository.
Server Config
{
"mcpServers": {
"taskflow": {
"command": "npx",
"args": [
"-y",
"@pinkpixel/taskflow-mcp"
],
"env": {
"TASK_MANAGER_FILE_PATH": "/path/to/tasks.json"
}
}
}
}