🧠 Recall — Memory Agent for Students

Amrita University MCP Hackathon 2026

Track: Education & Research

Team Members: [Insert Names & Roll Numbers]

Deployed URL: [Insert Nitrostack URL]

Demo Video: [Insert Video Link]

Problem & Solution

Recall is an MCP (Model Context Protocol) server that acts as a persistent memory agent for students. It captures narrated observations during labs, lectures, or fieldwork, stores them as structured, searchable memory, and lets the student — or an AI study assistant — recall them later across sessions.

Example: A chemistry student narrates "this is the titration setup for experiment 3, the flask needs to sit for 10 minutes" during a lab. Later, they (or their AI assistant) can recall that observation, get a study summary, or generate a lab report draft — all from the MCP server.

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🏗 Architecture

Built with the NitroStack MCP framework using a NestJS-style modular architecture:

recall/
├── src/
│   ├── index.ts                          # Bootstrap entry point
│   ├── app.module.ts                     # Root @McpApp module
│   └── modules/
│       ├── storage/
│       │   ├── storage.module.ts          # Global storage module
│       │   └── database.service.ts        # SQLite wrapper (sql.js)
│       └── recall/
│           ├── recall.module.ts           # Feature module
│           ├── recall.tools.ts            # 5 MCP tools
│           ├── recall.resources.ts        # Session graph resource
│           ├── recall.prompts.ts          # 2 prompt templates
│           ├── observation.service.ts     # Business logic service
│           └── types.ts                   # TypeScript interfaces
├── data/                                  # SQLite DB (auto-created)
├── package.json
├── tsconfig.json
├── .env / .env.example
└── README.md

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🔌 MCP Primitives

Tools (5)

Tool	Description
capture_observation	Store a new observation with label, note, context, and session ID
recall	Search observations by keyword, optionally scoped to a session
update_observation	Edit an existing observation's note
delete_observation	Soft-delete an observation
list_sessions	List all sessions with counts and date ranges

Resources (1)

URI	Description
recall://sessions/{session_id}/graph	Read-only structured dump of all observations in a session with inferred relations

Prompts (2)

Prompt	Description
study_summary	Generates a concise study summary from session observations
lab_report_draft	Turns session observations into a structured lab report skeleton

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🚀 Getting Started

Prerequisites

• Node.js ≥ 20.18.1 (use nvm to manage versions)
• npm ≥ 9
• tsx installed globally: npm i tsx -g

Install & Run

# 1. Install dependencies
npm install

# 2. Start the dev server
npx @nitrostack/cli dev

The server starts in stdio mode, ready for any MCP client (NitroStudio, Claude Desktop, etc.) to connect.

Environment Variables

Copy .env.example to .env and adjust if needed:

DB_PATH=./data/recall.db

The SQLite database and data/ directory are created automatically on first run.

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📋 Example Usage Flow

1. Capture observations during a lab

→ capture_observation({
    label: "titration setup",
    note: "Flask with 25ml NaOH, add phenolphthalein indicator, 3 drops",
    context: "experiment-3",
    session_id: "chem-lab-2026-07-18"
  })
← { id: "abc-123", timestamp: "2026-07-18T10:30:00Z" }

→ capture_observation({
    label: "titration endpoint",
    note: "Color changed from pink to colorless at 22.4ml HCl",
    context: "experiment-3",
    session_id: "chem-lab-2026-07-18"
  })
← { id: "def-456", timestamp: "2026-07-18T10:45:00Z" }

2. Recall observations later

→ recall({ query: "titration", session_id: "chem-lab-2026-07-18" })
← { count: 2, results: [ ... ] }

3. Browse session graph

→ read resource: recall://sessions/chem-lab-2026-07-18/graph
← { observations: [...], relations: [...], metadata: { ... } }

4. Generate a study summary

→ use prompt: study_summary({ session_id: "chem-lab-2026-07-18", topic: "titration" })
← [Formatted prompt with observations embedded, asking AI to produce a study summary]

5. Draft a lab report

→ use prompt: lab_report_draft({ session_id: "chem-lab-2026-07-18" })
← [Formatted prompt asking AI to produce Objective → Method → Observations → Conclusion]

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🏆 Hackathon Rubric Alignment

Criterion	How Recall Addresses It
MCP Primitives	All 3 implemented: Tools (5), Resources (1), Prompts (2)
Education & Research	Memory agent designed for students in labs, lectures, fieldwork
Code Quality	TypeScript strict mode, NitroStack decorators, DI, modular architecture
Architecture	Clean separation: storage → service → tools/resources/prompts
Runnable Locally	npm install && npx @nitrostack/cli dev — zero external dependencies
Storage	SQLite via sql.js (WASM-based, auto-schema, swappable)

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🔮 Future Enhancements (if time allows)

• Semantic similarity search (embeddings + vector store)
• Camera/mic streaming for real-time observation capture
• Canvas UI for visual browsing of observation graphs
• Notion/Slack/Gmail integrations for exporting summaries
• Graph DB backend (Neo4j/SurrealDB) for richer relation queries

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📄 License

MIT