Skip to main content
Glama
sree-24066

Recall — Memory Agent for Students

by sree-24066

🧠 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.


Related MCP server: clova-speech-lecture-mcp

🏗 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

🔌 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


🚀 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.


📋 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]

🏆 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)


🔮 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


📄 License

MIT

A
license - permissive license
Not graded
quality - not tested
B
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

View all related MCP servers

Related MCP Connectors

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sree-24066/amrita-mcp-recall'

If you have feedback or need assistance with the MCP directory API, please join our Discord server