productivity

TaskManager

kazuph

by kazuph

TaskManager streamlines project tracking and time management with efficient task queues, ideal for managing projects sof

Manage and execute tasks efficiently with MCP TaskManager in a queue-based system. This server supports planning by accepting and organizing task lists, and execution by delivering tasks one at a time with feedback on completion. It tracks tasks via unique IDs, ensuring smooth workflow coordination through clear stages: planning, executing, and completing. Designed for integration with MCP clients like Claude Desktop, TaskManager simplifies complex task handling and improves automation by providing structured task queues and real-time updates. Its straightforward action parameters allow easy control over task flow, making it a powerful tool for effective task management.

github stars

213

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Queue-based task executionApproval workflow for task completionReal-time progress tracking

best for

  • / Breaking down complex projects into manageable tasks
  • / Coordinating multi-step workflows with Claude Desktop
  • / Tracking progress on sequential task execution

capabilities

  • / Plan and organize task lists from user requests
  • / Execute tasks sequentially from a queue
  • / Track task completion with approval workflow
  • / Update or delete incomplete tasks
  • / List all requests and their task status
  • / Add new tasks to existing requests

what it does

Manages tasks in a queue-based system where you can plan task lists, execute them one by one, and track completion status with unique IDs.

about

TaskManager is a community-built MCP server published by kazuph that provides AI assistants with tools and capabilities via the Model Context Protocol. TaskManager streamlines project tracking and time management with efficient task queues, ideal for managing projects sof It is categorized under productivity. This server exposes 10 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install TaskManager in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

TaskManager is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

MCP TaskManager

Model Context Protocol server for Task Management. This allows Claude Desktop (or any MCP client) to manage and execute tasks in a queue-based system.

<a href="https://glama.ai/mcp/servers/bdjh7kx05h"><img width="380" height="200" src="https://glama.ai/mcp/servers/bdjh7kx05h/badge" alt="@kazuph/mcp-taskmanager MCP server" /></a>

Quick Start (For Users)

Prerequisites

Configuration

  1. Open your Claude Desktop configuration file at: ~/Library/Application Support/Claude/claude_desktop_config.json

You can find this through the Claude Desktop menu:

  1. Open Claude Desktop

  2. Click Claude on the Mac menu bar

  3. Click "Settings"

  4. Click "Developer"

  5. Add the following to your configuration:

{
  "tools": {
    "taskmanager": {
      "command": "npx",
      "args": ["-y", "@kazuph/mcp-taskmanager"]
    }
  }
}

For Developers

Prerequisites

  • Node.js 18+ (install via brew install node)
  • Claude Desktop (install from https://claude.ai/desktop)
  • tsx (install via npm install -g tsx)

Installation

git clone https://github.com/kazuph/mcp-taskmanager.git
cd mcp-taskmanager
npm install
npm run build

Development Configuration

  1. Make sure Claude Desktop is installed and running.

  2. Install tsx globally if you haven't:

npm install -g tsx
# or
pnpm add -g tsx
  1. Modify your Claude Desktop config located at: ~/Library/Application Support/Claude/claude_desktop_config.json

Add the following to your MCP client's configuration:

{
  "tools": {
    "taskmanager": {
      "args": ["tsx", "/path/to/mcp-taskmanager/index.ts"]
    }
  }
}

Available Operations

The TaskManager supports two main phases of operation:

Planning Phase

  • Accepts a task list (array of strings) from the user
  • Stores tasks internally as a queue
  • Returns an execution plan (task overview, task ID, current queue status)

Execution Phase

  • Returns the next task from the queue when requested
  • Provides feedback mechanism for task completion
  • Removes completed tasks from the queue
  • Prepares the next task for execution

Parameters

  • action: "plan" | "execute" | "complete"
  • tasks: Array of task strings (required for "plan" action)
  • taskId: Task identifier (required for "complete" action)
  • getNext: Boolean flag to request next task (for "execute" action)

Example Usage

// Planning phase
{
  action: "plan",
  tasks: ["Task 1", "Task 2", "Task 3"]
}

// Execution phase
{
  action: "execute",
  getNext: true
}

// Complete task
{
  action: "complete",
  taskId: "task-123"
}

FAQ

What is the TaskManager MCP server?
TaskManager is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for TaskManager?
This profile displays 64 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

List & Promote Your MCP Server

Share your MCP server with the developer community

GET_STARTED →
MCP server reviews

Ratings

4.764 reviews
  • Dhruvi Jain· Dec 20, 2024

    We evaluated TaskManager against two servers with overlapping tools; this profile had the clearer scope statement.

  • Kaira Singh· Dec 20, 2024

    We wired TaskManager into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Kaira Rao· Dec 16, 2024

    Useful MCP listing: TaskManager is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Henry Rahman· Dec 8, 2024

    We evaluated TaskManager against two servers with overlapping tools; this profile had the clearer scope statement.

  • Kaira Patel· Dec 4, 2024

    I recommend TaskManager for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Kiara Khan· Nov 27, 2024

    TaskManager has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Oshnikdeep· Nov 11, 2024

    TaskManager has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Kaira Thomas· Nov 11, 2024

    Strong directory entry: TaskManager surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Kaira Anderson· Nov 11, 2024

    TaskManager reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Noah Abbas· Nov 7, 2024

    TaskManager is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

showing 1-10 of 64

1 / 7