Task Manager MCP Server

Name: Task Manager MCP Server
Author: tradesdontlie

@tradesdontlie

Repository

About Task Manager MCP Server

A task management MCP server that provides comprehensive project and task tracking capabilities

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "task-manager-mcp": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "-e",
        "."
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.