What is the MCP Server?

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

How does the MCP Server work?

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

What are the key features of the MCP Server?

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

Which MCP clients are supported?

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

How do I install the MCP Server?

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

Can I customize the AI roles?

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

What kind of maintenance does the MCP Server perform?

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

What are some common use cases for the MCP Server?

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

How does the MCP Server integrate with UBOS?

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.

Where can I find the documentation?

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