Task Manager MCP Service

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An intelligent task management service based on Model Context Protocol (MCP), helping to automate project task breakdown, dependency management, and execution recommendations.

Features

• Automated Task Breakdown: Automatically extract and plan task structures from PRD documents

• Dependency Management: Intelligently handle dependencies between tasks, avoiding circular dependencies

• Smart Task Recommendations: Recommend the next task to execute based on dependency status and priority

• Subtask Expansion: Use LLM to automatically expand main tasks into detailed subtasks

• Code Association: Record associations between tasks and implementation code for better traceability

• Task Priority: Support multi-level task priorities and tag management

• MCP Integration: Native support for Model Context Protocol (MCP) for easy collaboration with LLMs

Related MCP server: Kiro MCP Memory

Quick Start

Installation

Recommended installation using uv:

# Install uv
pip install uv

# Install dependencies
uv pip install -r requirements.txt

Or use traditional pip installation:

pip install fastmcp uvicorn pydantic google-generativeai

Environment Configuration

Set necessary environment variables:

# Gemini configuration
export GEMINI_API_KEY="your-api-key-here"
export LLM_PROVIDER="gemini"
export MODEL_NAME="gemini-1.5-flash"

# Or OpenAI configuration
# export OPENAI_API_KEY="your-api-key-here"
# export LLM_PROVIDER="openai"
# export MODEL_NAME="gpt-4o"

# Optional: proxy settings
export HTTP_PROXY="http://your-proxy:port"
export HTTPS_PROXY="http://your-proxy:port"

# Optional: output directory
export MCP_OUTPUT_DIR="/path/to/output"

Basic Usage

1. Start the service (using uv):

uv run --with fastmcp fastmcp run src/server.py

Or directly using Python:

cd src
python server.py 

2. Configure MCP service in Cursor IDE:

Edit the ~/.cursor/mcp.json file (usually located at C:\Users\<username>\.cursor\mcp.json), find the mcpServers section and add or update the task-manager configuration:

{
  "mcpServers": {
    "task-manager": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "fastmcp",
        "fastmcp",
        "run",
        "D:\\code\\git_project\\task-manager-mcp\\src\\server.py" // Note: Replace this with the absolute path to your local server.py
      ],
      "env": {
        "GEMINI_API_KEY": "<Your Gemini API Key>",
        "HTTP_PROXY": "http://127.0.0.1:7890", // If proxy is needed
        "HTTPS_PROXY": "http://127.0.0.1:7890", // If proxy is needed
        "MODEL_NAME": "gemini-1.5-flash", // Or other supported models
        "LLM_PROVIDER": "gemini", // Or openai
        "MCP_OUTPUT_DIR": "D:\\path\\to\\your\\output\\directory\\" // Optional: specify output directory
      }
    }
    // There might be other server configurations...
  }
}

Note:

• Replace D:\code\git_project\task-manager-mcp\src\server.py with the absolute path to your local server.py file.

• Ensure the environment variables in env are set correctly, especially the API key and proxy settings (if needed).

• MCP_OUTPUT_DIR is optional, used to specify the output location for task-related files (such as JSON, Markdown).

3. Using the service in Cursor:

@task-manager decompose_prd prd_content="file:///D:/path/to/prd.md"

Documentation

For detailed documentation, please refer to the docs/ directory:

• Design Document - System design overview

• Getting Started - Getting started guide

• API Reference - Detailed API reference

• MCP Rules - LLM calling conventions

• Configuration Examples - Configuration examples

• Implementation Guide - Implementation guide

• Installation Guide - Detailed installation instructions

• To-Do List - Development roadmap

Key Functions

PRD Parsing and Task Breakdown

Automatically extract tasks and dependencies from project requirement documents:

@task-manager decompose_prd prd_content="file:///D:/path/to/prd.md"

Task Management

Create and update tasks (including status, dependencies, code references, etc.):

@task-manager add_task name="Implement login function" description="Implement user login function, including form validation" priority="high" tags="frontend,user function"

@task-manager update_task task_id="1" status="in_progress" dependencies="2,3"

@task-manager get_task task_id="1"

@task-manager get_task_list status="todo" priority="high" tag="frontend"

Subtask Expansion

Expand a main task into multiple subtasks:

@task-manager expand_task task_id="1" num_subtasks="5"

Smart Task Recommendation

Get the next task that should be executed:

@task-manager get_next_executable_task

Code Reference Management

Update code files associated with a task:

@task-manager update_task_code_references task_id="1" code_files="src/login.js,src/utils/validation.js"

System Architecture

The system is built on Python and the FastMCP framework, providing interfaces using MCP tools. The architecture includes:

• MCP Service Layer: Handles MCP protocol communication and tool invocation

• Core Logic Layer: Implements core features such as task management and dependency checking

• LLM Integration Layer: Integrates with LLM services like Gemini/OpenAI for intelligent parsing

• Storage Layer: Supports in-memory storage (default) and database storage (extensible)

Contributing

Contributions are welcome, including code contributions, issue reports, or new feature suggestions. Please refer to the Contributing Guide for details.

License

MIT License