Question 1

What is the MCP Server?

Accepted Answer

The MCP Server is a Model Context Protocol server that orchestrates AI tasks, breaking down complex projects into structured workflows with specialized AI roles.

Question 2

How does the MCP Server work?

Accepted Answer

It uses a five-step process: Workspace Detection, Task Analysis, Task Planning, Specialist Execution, and Result Synthesis.

Question 3

What are the key features of the MCP Server?

Accepted Answer

Key features include LLM-powered task decomposition, specialist AI roles, automated maintenance, task persistence, and workspace intelligence.

Question 4

Which MCP clients are supported?

Accepted Answer

The MCP Server supports Claude Desktop, Cursor IDE, Windsurf, and VS Code with the Cline extension.

Question 5

How do I install the MCP Server?

Accepted Answer

You can install it from PyPI using `pip install mcp-task-orchestrator` or from source by cloning the GitHub repository.

Question 6

Can I customize the AI roles?

Accepted Answer

Yes, you can customize the AI roles by editing the `.task_orchestrator/roles/project_roles.yaml` file.

Question 7

What kind of maintenance does the MCP Server perform?

Accepted Answer

The MCP Server includes automatic cleanup, performance optimization, structure validation, handover preparation, and health monitoring.

Question 8

What are some common use cases for the MCP Server?

Accepted Answer

Common use cases include software development, data science, and documentation & content creation.

Question 9

How does the MCP Server integrate with UBOS?

Accepted Answer

The MCP Server seamlessly integrates with the UBOS platform, leveraging its capabilities to provide a comprehensive AI agent development environment, enhancing task orchestration within UBOS workflows.

Question 10

Where can I find the documentation?

Accepted Answer

Complete documentation is available in the `docs/` directory of the GitHub repository.

Task Orchestrator

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