Inspiration
We wanted to explore how an AI-powered productivity assistant could make everyday task management simpler and more organized. This inspired us to build Alexa++, a prototype that demonstrates how an assistant can interact with tools through the Model Context Protocol (MCP).
What it does
Alexa++ provides a simple web dashboard for managing tasks. Users can:
- Create new tasks.
- View and track tasks.
- Filter tasks by status.
- Mark tasks as completed.
- Monitor task progress through a dashboard.
How we built it
We built Alexa++ using Python, Streamlit, SQLite, and the Model Context Protocol (MCP). The MCP server exposes task-management tools through Streamable HTTP, while the web dashboard provides an interactive interface for users to manage their tasks.
Challenges we faced
One of our main challenges was connecting the web application to the MCP server and ensuring that task operations worked correctly. We also worked through environment setup, server connectivity, and dashboard integration while building the prototype.
What we learned
This project helped us understand MCP tool integration, Streamable HTTP communication, database operations, and interactive dashboard development. It also helped us explore how connected tools and Agent Skills can contribute to more capable productivity assistants.
What's next
We plan to improve Alexa++ by exploring Agent Skills integration, more natural-language interactions, reminders, and additional productivity features.
Log in or sign up for Devpost to join the conversation.