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Frontend
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Giving Task
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Getting Output
Inspiration
What it does
How we built it
About DayMate MCP
DayMate MCP is an AI-powered task-planning application built to help people turn overwhelming goals into manageable, actionable tasks.
Inspiration
Planning a goal is often easy; breaking it into practical steps and following through can be difficult. I built DayMate MCP to make that process easier by combining AI-generated plans with an interactive task-management workflow.
How I built it
The application uses Python and Streamlit for the user interface, OpenRouter for AI-generated task plans, and a self-hosted Model Context Protocol (MCP) server using Streamable HTTP. The server exposes three tools: add_task, list_tasks, and complete_task. Users can review proposed tasks before adding them and manage tasks through the dashboard.
I deployed the MCP backend on Render and added automated testing and GitHub Actions CI/CD to improve reliability and simplify development.
Challenges and learning
One challenge was handling inconsistent AI responses, including JSON wrapped in Markdown or additional text. I improved the planner's response parsing to handle these formats more robustly. I also learned how to expose application functionality through MCP tools, connect a frontend to a deployed backend, and validate changes using automated tests.
What's next
Future improvements include persistent task storage, richer task metadata, authentication, and additional assistant integrations.
DayMate MCP demonstrates a practical MCP-powered assistant workflow through a web application. It is a simulated assistant experience, not a claim of native Alexa device integration.
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for DayMate MCP — AI Task Planning Assistant
Built With
- ai
- generative-ai
- github-actions
- mcp
- model-context-protocol
- openrouter
- python
- render
- streamlit
