Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Study Pilot I've got an hour, what should I study?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Track: Alexa+ (self-hosted MCP server) · MCP spec: 2025-11-25 · Transport: Streamable HTTP
Students tell Alexa about their exams once. Study Pilot remembers the exams, syllabus topics and every study session across conversations, and answers the question students actually ask every day: "I have an hour — what should I study?"
Tool | What you'd say to Alexa+ | What happens |
| "My Calculus exam is on 16 November: limits, derivatives…" | Saves the exam and its topics |
| "I did 45 minutes of Calculus and finished limits" | Logs time, ticks off the topic |
| "I've got an hour, what should I study?" | Picks the most urgent subject + topic |
| "Plan the next 5 days, 2 hours a day" | Day-by-day timetable in 15-min blocks |
| "How am I doing?" | Days left, topics done, minutes studied |
| "Remove Physics, the exam's over" | Deletes it (marked |
Every tool returns structured output plus a short speech field written to be read aloud.
Also exposes a resource (studypilot://exams) and a prompt (daily_checkin).
urgency = (topics left + 1) / (days left + 1) / (1 + minutes studied in last 3 days / 60)More topics left and fewer days left push a subject up; studying it recently pushes it down, so subjects rotate. In plan_revision, the day before an exam is reserved entirely for that subject.
Related MCP server: Syllabuddy
python -m venv .venv
# Windows: .venv\Scripts\activate macOS/Linux: source .venv/bin/activate
pip install -r requirements.txt
python server.py --reset # server at http://127.0.0.1:8000/mcpIn a second terminal (same venv):
python demo_alexa.py # simulated Alexa+ conversation against the real serverHealth check: http://127.0.0.1:8000/health. Change host/port with the HOST and PORT environment variables. Data is stored in data/study_data.json (override with STUDY_PILOT_DATA).
Point a Streamable HTTP MCP client at http://127.0.0.1:8000/mcp. For example, with MCP Inspector:
npx @modelcontextprotocol/inspector
# Transport: Streamable HTTP, URL: http://127.0.0.1:8000/mcpAlexa+ turns speech into MCP tool calls. demo_alexa.py plays that orchestrator role with scripted turns so judges can see each tools/call and the spoken reply. The server is real: it negotiates protocol 2025-11-25 and the second conversation opens a new MCP session to show that memory survives across sessions.
server.py — the MCP server (tools, resource, prompt, health route, JSON storage)
demo_alexa.py — simulated Alexa+ conversation using the official MCP Python client
requirements.txt — pinned mcp[cli]==1.30.0
MIT
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