Task Manager MCP Server

What is Task Manager MCP Server?

A template implementation of the Model Context Protocol (MCP) server for managing tasks and projects, providing task management, project organization, task tracking, and PRD parsing. It enables AI agents to manage tasks, track project progress, and break down Product Requirements Documents (PRDs) into actionable tasks. Serves as a practical template for building custom MCP servers with task management capabilities.

How to use Task Manager MCP Server?

Install using uv or Docker, configure environment variables in a .env file (including TRANSPORT, HOST, PORT), then run with python3 src/main.py or the Docker container. Use the provided tools (e.g., create_task_file, add_task, update_task_status, parse_prd) to manage projects and tasks programmatically.

Key features of Task Manager MCP Server

• create_task_file, add_task, update_task_status, get_next_task for task management
• parse_prd converts PRDs into structured tasks automatically
• expand_task breaks tasks into smaller subtasks
• estimate_task_complexity and get_task_dependencies for project planning
• generate_task_file creates file templates from task descriptions
• suggest_next_actions provides AI‑powered next‑step recommendations

Use cases of Task Manager MCP Server

• Automatically decompose a Product Requirements Document into a structured task list
• Track progress of tasks and subtasks within a project
• Retrieve the next uncompleted task to maintain workflow focus
• Estimate task complexity and time requirements for planning
• Generate file templates based on task descriptions to speed up development

FAQ from Task Manager MCP Server

What are the prerequisites to run the server?

Python 3.12+ is required, along with API keys for an LLM provider (OpenAI, OpenRouter, or Ollama). Docker is recommended but optional.

How do I configure the transport protocol?

Set the TRANSPORT environment variable to sse or stdio. For SSE, also set HOST and PORT (default 0.0.0.0:8050).

How can I integrate this server with MCP clients?

Use the provided JSON configuration for either SSE (URL http://localhost:8050/sse) or stdio (command python3 src/main.py with environment variables).

Can I extend or customize the Task Manager MCP Server?

Yes. The template is designed to be extended—add custom tools with the @mcp.tool() decorator, implement project‑specific templates, or integrate with existing development tools.

Where are task data stored?

The README does not specify a storage backend; data persistence is not described.