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

Обзор

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. 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. The server provides several essential task management tools: 1. - 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.

README

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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)

  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

# Set TRANSPORT=sse in .env then:
python3 src/main.py
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Установка

docker run --env-file .env -p 8050:8050 task-manager-mcp
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Конфигурация

{ "mcpServers": { "task-manager": { "transport": "sse", "url": "http://localhost:8050/sse" } } }