Divide and Conquer MCP Server
local-only server
The server can only run on the client’s local machine because it depends on local resources.
Integrations
Mentioned in the context of authentication implementation, where the current system uses express-session with MongoDB store.
Used as a storage solution for session-based authentication, mentioned in example context for a refactoring task.
Supports JWT implementation for Node.js applications, allowing for token structure, storage, and refresh mechanisms according to best practices.
Divide and Conquer MCP Server
A Model Context Protocol (MCP) server that enables AI agents to break down complex tasks into manageable pieces using a structured JSON format.
Table of Contents
- Purpose
- Key Features
- Quick Start
- Installation
- Tools
- Usage Examples
- Use Cases
- Configuration Storage
- Contributing
- License
Purpose
The Divide and Conquer MCP Server is an evolution of the Temp Notes MCP Server, designed specifically for complex tasks that need to be broken down into manageable pieces. Instead of using a simple text file, this server uses a structured JSON format to store task information, checklists, and context, making it easier to track progress and maintain context across multiple conversations.
Key Features
- Structured JSON Format: Instead of plain text, uses a JSON structure to store task information
- Task Tracking: Includes checklist functionality with completion status tracking
- Context Preservation: Dedicated fields for task context and detailed descriptions
- Progress Monitoring: Easy visualization of completed vs. remaining tasks
- Task Ordering: Maintains the order of tasks for sequential execution
- Task Insertion: Ability to insert new tasks at specific positions in the checklist
- Metadata: Track additional information like tags, priority, and estimated completion time
- Notes and Resources: Store additional notes and resources related to the task
Quick Start
- Add the server to your MCP configuration:Copy
- Start using it in your conversations:Copy
Installation
Option 1: Using npx (Recommended)
Add the server to your MCP configuration:
Option 2: Install from source
- Clone the repository:Copy
- Install dependencies:Copy
- Build the server:Copy
- Add the server to your MCP configuration:Copy
Tools
The Divide and Conquer MCP Server provides the following tools:
initialize_task
Creates a new task with the specified description and optional initial checklist items.
update_task_description
Updates the main task description.
update_context
Updates the context information for all tasks.
add_checklist_item
Adds a new item to the checklist.
update_checklist_item
Updates an existing checklist item.
mark_task_done
Marks a checklist item as done.
mark_task_undone
Marks a checklist item as not done.
remove_checklist_item
Removes a checklist item.
reorder_checklist_item
Moves a checklist item to a new position.
add_note
Adds a note to the task.
add_resource
Adds a resource to the task.
update_metadata
Updates the task metadata.
clear_task
Clears the current task data.
get_checklist_summary
Returns a summary of the checklist with completion status. Context information is intentionally excluded from the summary to save context window space.
get_current_task_details
Retrieves details of the current task (first uncompleted task) with full context, along with all other tasks with limited fields. For the current task, all fields including context_and_plan are included. For other tasks, only task, detailed_description, and done status are included (context_and_plan is excluded). This is the recommended tool to use when working with tasks.
Usage Examples
Initializing a Complex Task
Getting a Checklist Summary
Getting Current Task Details
Use Cases
1. Complex Software Development Tasks
When working on complex software development tasks, AI agents often face context window limitations that make it difficult to complete all steps in a single conversation. The Divide and Conquer MCP Server allows agents to:
- Break down large tasks into smaller, manageable pieces
- Track progress across multiple conversations
- Maintain important context that would otherwise be lost
- Organize tasks in a logical sequence
- Document decisions and resources
2. Project Planning and Management
For project planning and management tasks, the server enables:
- Creating structured project plans with tasks and subtasks
- Tracking progress and completion status
- Maintaining context and requirements
- Documenting decisions and resources
- Collaborating across multiple conversations
3. Research and Analysis
When conducting research and analysis, agents can:
- Break down research questions into specific areas to investigate
- Track progress and findings
- Maintain context and background information
- Document sources and resources
- Organize findings in a structured way
JSON Structure
The server uses the following JSON structure to store task information:
Configuration Storage
By default, the Divide and Conquer MCP Server stores task data in the following location:
- On macOS/Linux:
~/.mcp_config/divide_and_conquer.json
(which expands to/Users/username/.mcp_config/divide_and_conquer.json
) - On Windows:
C:\Users\username\.mcp_config\divide_and_conquer.json
This file is created automatically when you first initialize a task. If the file doesn't exist when you try to read task data, the server will return an empty task structure and create the file when you write to it next time.
The server handles the following scenarios:
- If the file doesn't exist when reading: Returns an empty task structure
- If the directory doesn't exist: Creates the directory structure automatically when writing
- If the file is corrupted or inaccessible: Returns appropriate error messages
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the MIT License - see the LICENSE file for details.
This server cannot be installed
Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.