Task Manager MCP Server
About
A task management MCP server that provides comprehensive project and task tracking capabilities
Details
- Author
- tradesdontlie
- GitHub stars
- 36
- Downloads
- 458
- Categories
- Productivity
Jump to
- 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
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Task Manager MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"task manager mcp server": {
"task-manager-mcp": {
"command": "uv",
"args": [
"pip",
"install",
"-e",
"."
]
}
}
}
}McpServers
{
"task-manager-mcp": {
"command": "uv",
"args": [
"pip",
"install",
"-e",
"."
]
}
}Task Manager MCP Server
A template implementation of the Model Context Protocol (MCP) server for managing tasks and projects. This server provides a comprehensive task management system with support for project organization, task tracking, and PRD parsing.
Overview
This project demonstrates how to build an MCP server that enables AI agents to manage tasks, track project progress, and break down Product Requirements Documents (PRDs) into actionable tasks. It serves as a practical template for creating your own MCP servers with task management capabilities.
The implementation follows the best practices laid out by Anthropic for building MCP servers, allowing seamless integration with any MCP-compatible client.
Features
The server provides several essential task management tools:
1. Task Management
- create_task_file: Create new project task files
- add_task: Add tasks to projects with descriptions and subtasks
- update_task_status: Update the status of tasks and subtasks
- get_next_task: Get the next uncompleted task from a project
2. Project Planning
- parse_prd: Convert PRDs into structured tasks automatically
- expand_task: Break down tasks into smaller, manageable subtasks
- estimate_task_complexity: Estimate task complexity and time requirements
- get_task_dependencies: Track task dependencies
3. Development Support
- generate_task_file: Generate file templates based on task descriptions
- suggest_next_actions: Get AI-powered suggestions for next steps
Prerequisites
- Python 3.12+
- API keys for your chosen LLM provider (OpenAI, OpenRouter, or Ollama)
- Docker if running the MCP server as a container (recommended)
Installation
Using uv
1. Install uv if you don't have it:
pip install uv
2. Clone this repository:
git clone https://github.com/coleam00/mcp-mem0.git
cd mcp-mem0
3. Install dependencies:
uv pip install -e .
4. Create a .env file based on .env.example:
cp .env.example .env
5. Configure your environment variables in the .env file (see Configuration section)
Using Docker (Recommended)
1. Build the Docker image:
docker build -t mcp/mem0 --build-arg PORT=8050 .
2. Create a .env file based on .env.example and configure your environment variables
Configuration
The following environment variables can be configured in your .env file:
| Variable | Description | Example |
|----------|-------------|----------|
| TRANSPORT | Transport protocol (sse or stdio) | sse |
| HOST | Host to bind to when using SSE transport | 0.0.0.0 |
| PORT | Port to listen on when using SSE transport | 8050 |
Running the Server
Using Python 3
```bash
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