Chain of Thought Task Manager

Chain of Thought Task Manager

liorfranko

Converts natural language task descriptions into organized development plans with dependency tracking, step-by-step implementation guides, and verification criteria.

Task management system that converts natural language into organized development tasks with dependency tracking, implementation guides, and verification criteria through structured reasoning phases.

20415 views6Local (stdio)

What it does

  • Break down complex tasks into manageable subtasks
  • Track task dependencies and status
  • Generate implementation guides with verification criteria
  • Store task history for reference
  • Define project-specific rules and standards
  • Provide step-by-step reasoning for problem solving

Best for

Software developers planning complex featuresProject managers organizing development workflowsAI agents needing structured task executionTeams wanting consistent development standards
Intelligent task decomposition with dependency managementOptional web GUI for visual task managementBuilt-in verification and quality checks

About Chain of Thought Task Manager

Chain of Thought Task Manager is a community-built MCP server published by liorfranko that provides AI assistants with tools and capabilities via the Model Context Protocol. Organize projects using leading project track software. Convert tasks with dependency tracking for optimal time manageme It is categorized under productivity.

How to install

You can install Chain of Thought Task Manager 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

Chain of Thought Task Manager is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

MCP Chain of Thought

Chain of Thought Demo smithery badge

🚀 An intelligent task management system based on Model Context Protocol (MCP), providing an efficient programming workflow framework for AI Agents.

README image Link to glama.ai

📑 Table of Contents

✨ Features

  • 🧠 Task Planning & Analysis: Deep understanding of complex task requirements
  • 🧩 Intelligent Task Decomposition: Break down large tasks into manageable smaller tasks
  • 🔄 Dependency Management & Status Tracking: Handle dependencies and monitor progress
  • ✅ Task Verification: Ensure results meet requirements
  • 💾 Task Memory: Store task history for reference and learning
  • ⛓️ Thought Chain Process: Step-by-step reasoning for complex problems
  • 📋 Project Rules: Define standards to maintain consistency
  • 🌐 Web GUI: Optional web interface (enable with ENABLE_GUI=true)
  • 📝 Detailed Mode: View conversation history (enable with ENABLE_DETAILED_MODE=true)

🧭 Usage Guide

🚀 Quick Start

  1. 🔽 Installation: Install MCP Chain of Thought via Smithery or manually
  2. 🏁 Initial Setup: Tell the Agent "init project rules" to establish project-specific guidelines
  3. 📝 Plan Tasks: Use "plan task [description]" to create a development plan
  4. 👀 Review & Feedback: Provide feedback during the planning process
  5. ▶️ Execute Tasks: Use "execute task [name/ID]" to implement a specific task
  6. 🔄 Continuous Mode: Say "continuous mode" to process all tasks sequentially

🔍 Memory & Thinking Features

  • 💾 Task Memory: Automatically saves execution history for reference
  • 🔄 Thought Chain: Enables systematic reasoning through process_thought tool
  • 📋 Project Rules: Maintains consistency across your codebase

🔧 Installation

🔽 Via Smithery

npx -y @smithery/cli install @liorfranko/mcp-chain-of-thought --client claude

🔽 Manual Installation

npm install
npm run build

🔌 Using with MCP-Compatible Clients

⚙️ Configuration in Cursor IDE

Add to your Cursor configuration file (~/.cursor/mcp.json or project-specific .cursor/mcp.json):

{
  "mcpServers": {
    "chain-of-thought": {
      "command": "npx",
      "args": ["-y", "mcp-chain-of-thought"],
      "env": {
        "DATA_DIR": "/path/to/project/data", // Must use absolute path
        "ENABLE_THOUGHT_CHAIN": "true",
        "TEMPLATES_USE": "en",
        "ENABLE_GUI": "true",
        "ENABLE_DETAILED_MODE": "true"
      }
    }
  }
}

⚠️ Important: DATA_DIR must use an absolute path.

🔧 Environment Variables

  • 📁 DATA_DIR: Directory for storing task data (absolute path required)
  • 🧠 ENABLE_THOUGHT_CHAIN: Controls detailed thinking process (default: true)
  • 🌐 TEMPLATES_USE: Template language (default: en)
  • 🖥️ ENABLE_GUI: Enables web interface (default: false)
  • 📝 ENABLE_DETAILED_MODE: Shows conversation history (default: false)

🛠️ Tools Overview

CategoryToolDescription
📋 Planningplan_taskStart planning tasks
analyze_taskAnalyze requirements
process_thoughtStep-by-step reasoning
reflect_taskImprove solution concepts
init_project_rulesSet project standards
🧩 Managementsplit_tasksBreak into subtasks
list_tasksShow all tasks
query_taskSearch tasks
get_task_detailShow task details
delete_taskRemove tasks
▶️ Executionexecute_taskRun specific tasks
verify_taskVerify completion
complete_taskMark as completed

🤖 Recommended Models

  • 👑 Claude 3.7: Offers strong understanding and generation capabilities
  • 💎 Gemini 2.5: Google's latest model, performs excellently

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📚 Documentation

⭐ Star History

Star History Chart

Alternatives

Related Skills

Browse all skills
thought-patterns

Orchestrates comprehensive cognitive thinking patterns including sequential, systems, lateral, divergent/convergent, abductive, analogical, first-principles, double-loop learning, gestalt, metacognitive, and neurodivergent patterns (ADHD hyperfocus, autistic detail-orientation, dyslexic spatial reasoning). Analyzes tasks to select optimal pattern(s), chains multiple patterns when needed, and validates outputs before responding. Use for complex problem-solving, creative tasks, analytical challenges, or when diverse cognitive approaches enhance solution quality.

1
thought-based-reasoning

Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns

0
sequential-thinking

Use when complex problems require systematic step-by-step reasoning with ability to revise thoughts, branch into alternative approaches, or dynamically adjust scope. Ideal for multi-stage analysis, design planning, problem decomposition, or tasks with initially unclear scope.

31
agent-task-manager

Manages and orchestrates multi-step, stateful agent workflows; handles task dependencies, persistent state, error recovery, and external rate-limiting. Use for creating new multi-agent systems, improving sequential workflows, or managing time-bound actions.

0
taskmaster

Project manager and task delegation system. Use when you need to break down complex work into smaller tasks, assign appropriate AI models based on complexity, spawn sub-agents for parallel execution, track progress, and manage token budgets. Ideal for research projects, multi-step workflows, or when you want to delegate routine tasks to cheaper models while handling complex coordination yourself.

0
log-focus-debug

DashPlayer 日志聚焦调试技能。Use when developers ask to reduce noisy logs, focus on one feature log chain, add temporary focus markers (e.g. [FOCUS:token]), or clean up temporary debug logs after task completion. Triggers on: "日志太乱", "只看某个功能日志", "focus token", "withFocus", "临时日志标记", "清理调试日志".

0