Sponsored by Deepsite.site

Taskqueue

Created By
chriscarrollsmith9 months ago
Content

MCP Task Manager

A Model Context Protocol (MCP) server for AI task management. This tool helps AI assistants handle multi-step tasks in a structured way, with user approval checkpoints.

Features

  • Task planning with multiple steps
  • Progress tracking
  • User approval of completed tasks
  • Project completion approval
  • Task details visualization
  • Task status state management
  • Enhanced CLI for task inspection and management

Usage

Usually you will set the tool configuration in Claude Desktop, Cursor, or another MCP client as follows:

{
  "tools": {
    "taskqueue": {
      "command": "npx",
      "args": ["-y", "taskqueue-mcp"]
    }
  }
}

Or, with a custom tasks.json path:

{
  "tools": {
    "taskqueue": {
      "command": "npx",
      "args": ["-y", "taskqueue-mcp"],
      "env": {
        "TASK_MANAGER_FILE_PATH": "/path/to/tasks.json"
      }
    }
  }
}

To use the CLI utility, you can use the following command:

npx task-manager-cli --help

This will show the available commands and options.

Available Operations

The TaskManager now uses a direct tools interface with specific, purpose-built tools for each operation:

Project Management Tools

  • list_projects: Lists all projects in the system
  • read_project: Gets details about a specific project
  • create_project: Creates a new project with initial tasks
  • delete_project: Removes a project
  • add_tasks_to_project: Adds new tasks to an existing project
  • finalize_project: Finalizes a project after all tasks are done

Task Management Tools

  • list_tasks: Lists all tasks for a specific project
  • read_task: Gets details of a specific task
  • create_task: Creates a new task in a project
  • update_task: Modifies a task's properties (title, description, status)
  • delete_task: Removes a task from a project
  • approve_task: Approves a completed task
  • get_next_task: Gets the next pending task in a project
  • mark_task_done: Marks a task as completed with details

Task Status and Workflows

Tasks have a status field that can be one of:

  • not started: Task has not been started yet
  • in progress: Task is currently being worked on
  • done: Task has been completed (requires completedDetails)

Status Transition Rules

The system enforces the following rules for task status transitions:

  • Tasks follow a specific workflow with defined valid transitions:
    • From not started: Can only move to in progress
    • From in progress: Can move to either done or back to not started
    • From done: Can move back to in progress if additional work is needed
  • When a task is marked as "done", the completedDetails field must be provided to document what was completed
  • Approved tasks cannot be modified
  • A project can only be approved when all tasks are both done and approved

These rules help maintain the integrity of task progress and ensure proper documentation of completed work.

Usage Workflow

A typical workflow for an LLM using this task manager would be:

  1. create_project: Start a project with initial tasks
  2. get_next_task: Get the first pending task
  3. Work on the task
  4. mark_task_done: Mark the task as complete with details
  5. Wait for approval (user must call approve_task through the CLI)
  6. get_next_task: Get the next pending task
  7. Repeat steps 3-6 until all tasks are complete
  8. finalize_project: Complete the project (requires user approval)

CLI Commands

Task Approval

Task approval is controlled exclusively by the human user through the CLI command:

npm run approve-task -- <projectId> <taskId>

Options:

  • -f, --force: Force approval even if the task is not marked as done

Note: Tasks must be marked as "done" with completed details before they can be approved (unless using --force).

Listing Tasks and Projects

The CLI provides a command to list all projects and tasks:

npm run list-tasks

To view details of a specific project:

npm run list-tasks -- -p <projectId>

This command displays information about all projects in the system or a specific project, including:

  • Project ID and initial prompt
  • Completion status
  • Task details (title, description, status, approval)
  • Progress metrics (approved/completed/total tasks)

Example Usage

Creating a Project with Tasks

// Example of how an LLM would use the create_project tool
{
  'create_project': {
    'initialPrompt': "Create a website for a small business",
    'projectPlan': "We'll create a responsive website with Home, About, Services, and Contact pages",
    'tasks': [
      { 
        'title': "Set up project structure", 
        'description': "Create repository and initialize with basic HTML/CSS/JS files",
        'toolRecommendations': "create_directory, create_file, git_init",
        'ruleRecommendations': "Use consistent file naming, Initialize git repository"
      },
      { 
        'title': "Design homepage", 
        'description': "Create responsive homepage with navigation and hero section",
        'toolRecommendations': "html_editor, css_editor, image_optimizer",
        'ruleRecommendations': "Follow accessibility guidelines (WCAG), Optimize for mobile-first"
      },
      { 
        'title': "Implement about page", 
        'description': "Create about page with company history and team section",
        'toolRecommendations': "html_editor, css_editor",
        'ruleRecommendations': "Use clear and concise language, Include team member photos"
      }
  ]
}
}

// Response will include:
// {
//   status: "planned",
//   projectId: "proj-1234",
//   totalTasks: 3,
//   tasks: [
//     { id: "task-1", title: "Set up structure", ..., toolRecommendations: "...", ruleRecommendations: "..." },
//     { id: "task-2", title: "Design homepage", ..., toolRecommendations: "...", ruleRecommendations: "..." },
//     { id: "task-3", title: "Implement about page", ..., toolRecommendations: "...", ruleRecommendations: "..." }
//   ],
//   message: "Project created with 3 tasks"
// }

Getting the Next Task

// Example of how an LLM would use the get_next_task tool
{
  'get_next_task': {
    'projectId': "proj-1234"
  }
}

// Response will include:
// {
//   status: "next_task",
//   task: {
//     id: "task-1",
//     title: "Set up project structure",
//     description: "Create repository and initialize with basic HTML/CSS/JS files",
//     status: "not started",
//     approved: false
//   },
//   message: "Retrieved next task"
// }

Marking a Task as Done

// Example of how an LLM would use the mark_task_done tool
{
  'mark_task_done': {
    'projectId': "proj-1234",
    'taskId': "task-1",
    'completedDetails': "Created repository at github.com/example/business-site and initialized with HTML5 boilerplate, CSS reset, and basic JS structure."  // Required when marking as done
  }
}

// Response will include:
// {
//   status: "task_marked_done",
//   task: {
//     id: "task-1",
//     title: "Set up project structure",
//     status: "done",
//     approved: false,
//     completedDetails: "Created repository at github.com/example/business-site and initialized with HTML5 boilerplate, CSS reset, and basic JS structure."
//   },
//   message: "Task marked as done"
// }

Approving a Task (CLI-only operation)

This operation can only be performed by the user through the CLI:

npm run approve-task -- proj-1234 task-1

After approval, the LLM can check the task status using read_task or get the next task using get_next_task.

Finalizing a Project

// Example of how an LLM would use the finalize_project tool
// (Called after all tasks are done and approved)
{
  'finalize_project': {
    'projectId': "proj-1234"
  }
}

// Response will include:
// {
//   status: "project_finalized",
//   projectId: "proj-1234",
//   message: "Project has been finalized"
// }

Status Codes and Responses

All operations return a status code and message in their response:

Project Tool Statuses

  • projects_listed: Successfully listed all projects
  • planned: Successfully created a new project
  • project_deleted: Successfully deleted a project
  • tasks_added: Successfully added tasks to a project
  • project_finalized: Successfully finalized a project
  • error: An error occurred (with error message)

Task Tool Statuses

  • task_details: Successfully retrieved task details
  • task_updated: Successfully updated a task
  • task_deleted: Successfully deleted a task
  • task_not_found: Task not found
  • error: An error occurred (with error message)

Structure of the Codebase

src/
├── index.ts                   # Main entry point
├── client/
│   └── cli.ts                 # CLI for user task review and approval
├── server/
│   ├── TaskManager.ts         # Core service functionality
│   └── tools.ts               # MCP tool definitions
└── types/
    └── index.ts               # Type definitions and schemas

Data Schema and Storage

The task manager stores data in a JSON file with platform-specific default locations:

  • Default locations:
    • Linux: ~/.local/share/taskqueue-mcp/tasks.json (following XDG Base Directory specification)
    • macOS: ~/Library/Application Support/taskqueue-mcp/tasks.json
    • Windows: %APPDATA%\taskqueue-mcp\tasks.json (typically C:\Users\<username>\AppData\Roaming\taskqueue-mcp\tasks.json)
  • Custom location: Set via TASK_MANAGER_FILE_PATH environment variable
# Example of setting custom storage location
TASK_MANAGER_FILE_PATH=/path/to/custom/tasks.json npm start

The data schema is organized as follows:

TaskManagerFile
├── projects: Project[]
    ├── projectId: string            # Format: "proj-{number}"
    ├── initialPrompt: string        # Original user request text
    ├── projectPlan: string          # Additional project details
    ├── completed: boolean           # Project completion status
    └── tasks: Task[]                # Array of tasks
        ├── id: string               # Format: "task-{number}"
        ├── title: string            # Short task title
        ├── description: string      # Detailed task description
        ├── status: string           # Task status: "not started", "in progress", or "done"
        ├── approved: boolean        # Task approval status
        ├── completedDetails: string # Completion information (required when status is "done")
        ├── toolRecommendations: string # Suggested tools that might be helpful for this task
        └── ruleRecommendations: string # Suggested rules/guidelines to follow for this task

The system persists this structure to the JSON file after each operation.

Explanation of Task Properties:

  • id: A unique identifier for the task
  • title: A short, descriptive title for the task
  • description: A more detailed explanation of the task
  • status: The current status of the task (not started, in progress, or done)
  • approved: Indicates whether the task has been approved by the user
  • completedDetails: Provides details about the task completion (required when status is done)
  • toolRecommendations: A string containing suggested tools (by name or identifier) that might be helpful for completing this task. The LLM can use this to prioritize which tools to consider.
  • ruleRecommendations: A string containing suggested rules or guidelines that should be followed while working on this task. This can include things like "ensure all code is commented," "follow accessibility guidelines," or "use the company style guide". The LLM uses these to improve the quality of its work.

License

MIT

Server Config

{
  "mcpServers": {
    "taskqueue": {
      "command": "npx",
      "args": [
        "-y",
        "taskqueue-mcp"
      ]
    }
  }
}
Recommend Servers
TraeBuild with Free GPT-4.1 & Claude 3.7. Fully MCP-Ready.
Amap Maps高德地图官方 MCP Server
Zhipu Web SearchZhipu Web Search MCP Server is a search engine specifically designed for large models. It integrates four search engines, allowing users to flexibly compare and switch between them. Building upon the web crawling and ranking capabilities of traditional search engines, it enhances intent recognition capabilities, returning results more suitable for large model processing (such as webpage titles, URLs, summaries, site names, site icons, etc.). This helps AI applications achieve "dynamic knowledge acquisition" and "precise scenario adaptation" capabilities.
AiimagemultistyleA Model Context Protocol (MCP) server for image generation and manipulation using fal.ai's Stable Diffusion model.
Jina AI MCP ToolsA Model Context Protocol (MCP) server that integrates with Jina AI Search Foundation APIs.
Playwright McpPlaywright MCP server
Visual Studio Code - Open Source ("Code - OSS")Visual Studio Code
Howtocook Mcp基于Anduin2017 / HowToCook (程序员在家做饭指南)的mcp server,帮你推荐菜谱、规划膳食,解决“今天吃什么“的世纪难题; Based on Anduin2017/HowToCook (Programmer's Guide to Cooking at Home), MCP Server helps you recommend recipes, plan meals, and solve the century old problem of "what to eat today"
WindsurfThe new purpose-built IDE to harness magic
CursorThe AI Code Editor
Tavily Mcp
BlenderBlenderMCP connects Blender to Claude AI through the Model Context Protocol (MCP), allowing Claude to directly interact with and control Blender. This integration enables prompt assisted 3D modeling, scene creation, and manipulation.
TimeA Model Context Protocol server that provides time and timezone conversion capabilities. This server enables LLMs to get current time information and perform timezone conversions using IANA timezone names, with automatic system timezone detection.
Serper MCP ServerA Serper MCP Server
Context7Context7 MCP Server -- Up-to-date code documentation for LLMs and AI code editors
MCP AdvisorMCP Advisor & Installation - Use the right MCP server for your needs
MiniMax MCPOfficial MiniMax Model Context Protocol (MCP) server that enables interaction with powerful Text to Speech, image generation and video generation APIs.
DeepChatYour AI Partner on Desktop
ChatWiseThe second fastest AI chatbot™
Baidu Map百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
EdgeOne Pages MCPAn MCP service designed for deploying HTML content to EdgeOne Pages and obtaining an accessible public URL.