Abstract
Existing methods for personal work information management typically require complex integrations and manual data entry across multiple AI tools, messengers, and document applications. This leads to scattered information, difficulty in priority management, and repetitive context explanations. In this work, we propose a simpler approach: we first collect all work-related conversations and data using the Model Context Protocol (MCP), then orchestrate intelligent agents using LangGraph to automatically process, summarize, and provide daily briefings. We demonstrate that this strategy achieves seamless integration across multiple AI clients (ChatGPT Desktop, Claude Desktop/CODE, Cursor) and productivity tools (Gmail, Slack, Notion) while maintaining 3D consistency in data flow and user experience.