I’m a dedicated user of My Life Organized (MLO) and love how it keeps my tasks in sync across Android, iOS, Windows, and soon the web. I also rely on Shortwave Email and the Bee AI Wearable, where I’ve been using an MCP Server (via [beemcp on GitHub](https://github.com/OkGoDoIt/beemcp)) to seamlessly integrate my email and Bee. This setup transforms email into an intelligent extension of my workflow, allowing AI to create todos in Bee from emails, reference past decisions, and understand my priorities—all without switching platforms. I’d love to extend this capability to MLO by adding an AI MCP Server, and I believe it could take MLO to the next level. ### What is an AI MCP Server? The Model Context Protocol (MCP) is an open standard from Anthropic that lets AI models connect to apps and data sources through a universal interface. An MCP Server for MLO would expose its todo lists, priorities, and actions to AI assistants (e.g., in email or wearables), enabling them to read and interact with MLO data securely and efficiently. Learn more at Anthropic’s [MCP introduction]( https://www.anthropic.com/news/introducing-the-model-context-protocol). ### Advantages for MLO Adding an MCP Server would bring these benefits: - **Context-Aware AI**: AI assistants could see my MLO todos and priorities, incorporating them into responses in email or other platforms. - **Seamless Todo Creation**: I could create MLO todos directly from emails or conversations without leaving the app I’m in. - **Unified Workflow**: The AI would understand my full thought process across MLO, email, and Bee, eliminating context loss. - **Smarter Interactions**: By observing patterns in MLO, the AI could offer better suggestions and automate tasks based on my preferences. This mirrors what I enjoy with Bee and email via beemcp: todos from emails, full conversation context, and a system that gets smarter by seeing my workflow holistically. ### How to Implement It Implementing an MCP Server is straightforward with available resources: 1. **Understand MCP**: Check out the [MCP specification]( https://modelcontextprotocol.io/spec) for details. 2. **Build the Server**: Use [MCP SDKs](https://docs.modelcontextprotocol.io/sdks) (Python, TypeScript, etc.) to expose MLO’s data and actions (e.g., read todos, create tasks). 3. **Secure It**: MCP includes authentication to ensure only authorized AI platforms access the data. For inspiration, the [beemcp repository](https://github.com/OkGoDoIt/beemcp) shows how this works for Bee and could guide an MLO implementation. ### Why It Matters An MCP Server would make MLO a central hub for AI-driven productivity, enhancing its value across my devices and platforms. I’d be thrilled to see this integration and am happy to discuss further. Thanks for considering this! -Danny
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