How does it differ from the Temp Notes MCP Server?
It is an evolution of Temp Notes that uses a structured JSON format instead of plain text, enabling checklists, context fields, metadata, and progress tracking.
@landicefu
基本信息
暂无概览
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"divide-and-conquer": {
"command": "npx",
"args": [
"-y",
"@landicefu/divide-and-conquer-mcp-server"
],
"disabled": false
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。
A Model Context Protocol (MCP) server that enables AI agents to break down complex tasks into manageable pieces using a structured JSON format. It is designed for AI agents facing context window limitations when working on multi-step or multi‑conversation tasks.
Add the server to your MCP configuration using npx -y @landicefu/divide-and-conquer-mcp-server, then invoke tools such as initialize_task, add_checklist_item, get_current_task_details, and get_checklist_summary via use_mcp_tool. The server stores task data locally in ~/.mcp_config/divide_and_conquer.json.
It is an evolution of Temp Notes that uses a structured JSON format instead of plain text, enabling checklists, context fields, metadata, and progress tracking.
On macOS/Linux at `~/.mcp_config/divide_and_conquer.json` and on Windows at `C:\Users\username\.mcp_config\divide_and_conquer.json`. The file is created automatically on first write.
It selectively provides full context only for the current (first uncompleted) task, while other tasks are returned with limited fields, saving context window space.
No, it runs locally as a Node.js process and requires no external APIs or databases.
It offers 14 tools including `initialize_task`, `add_checklist_item`, `mark_task_done`, `get_current_task_details`, `get_checklist_summary`, and more for managing tasks, checklists, notes, resources, and metadata.
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