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Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.

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liorfranko/mcp-chain-of-thought

 
 

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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.

📑 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

Category Tool Description
📋 Planning plan_task Start planning tasks
analyze_task Analyze requirements
process_thought Step-by-step reasoning
reflect_task Improve solution concepts
init_project_rules Set project standards
🧩 Management split_tasks Break into subtasks
list_tasks Show all tasks
query_task Search tasks
get_task_detail Show task details
delete_task Remove tasks
▶️ Execution execute_task Run specific tasks
verify_task Verify completion
complete_task Mark 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

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Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.

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