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Jinansh230705

NCERT MCP

by Jinansh230705
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TypeScript
Hybrid

NCERT MCP

Note: This project is a TypeScript/Bun port inspired by the original work from hatchedland/ncert-mcp. An open-source Model Context Protocol (MCP) server and REST API that turns the entire NCERT/CBSE curriculum (Grades 1–12) into structured, queryable infrastructure.

Built for ed-tech companies that want to build on top of NCERT content — explanations, question generation, semantic search, and topic mapping — without rebuilding the data pipeline themselves.

What it does

Capability

Description

Semantic search

Vectorless search over NCERT chunks via Minisearch — each result includes topic highlight, Bloom's level, and pre-filled actions (explain, question, learning path)

Keyword search

BM25 search across all chapter PDFs

RAG explanations

Grade-aware explanations with Markdown structure, LaTeX formulas, Mermaid diagrams, and callout notes

Question generation

Structured MCQ / SAQ / LAQ with marking schemes, Bloom's tagging

Question papers

Full CBSE-pattern papers: class test → pre-board → board exam

Curriculum graph

Prerequisite edges across subjects — powers learning paths

Question bank

Persistent SQLite store; questions reused across papers, never re-generated

REST API / OpenAPI

All tools exposed as HTTP endpoints for ChatGPT Custom Actions & non-MCP clients

Remote MCP

Dedicated Express SSE server for Claude.ai remote connectors

Related MCP server: SBU Syllabus MCP

Architecture

  • Runtime: Bun + TypeScript

  • Web Framework: Hono + Zod (for OpenAPI spec generation)

  • Database: Better-SQLite3 (Metadata) + Minisearch (Vectorless Search Index)

  • AI/LLM Client: Universal openai SDK pointing to OpenRouter (Use Claude, Gemini, DeepSeek, etc)

  • Deployment: Cloudflare Workers (Serverless REST API and SSE) + Cloudflare D1 (Database)

MCP Tools (13 tools)

Textbook tools

Tool

Description

list_books

List available NCERT textbooks, filter by grade/subject

list_topics

Chapter titles for a textbook

get_chapter

Full extracted text of one chapter

get_chapter_metadata

Source URL, download date (fast, no PDF parse)

search_chapters

BM25 keyword search across all PDFs

Search tools

Tool

Description

search_content

Semantic search with grade/subject/Bloom's filters. Returns scored results with pre-filled action params

get_curriculum_map

Topics + Bloom's level distribution per chapter

RAG + Generation tools

Tool

Description

generate_explanation

RAG-grounded explanation — Markdown, LaTeX, Mermaid diagrams

generate_question

Single structured question (MCQ/SAQ/LAQ) with marking scheme

generate_question_paper

Full CBSE-pattern paper (class test → board exam)

list_exam_types

List supported exam types with marks, duration, sections

Curriculum Graph tools

Tool

Description

get_prerequisites

Direct prerequisite topics for a given topic

get_learning_path

Full ordered prerequisite chain, roots first

REST API Endpoints

Method

Path

Description

GET

/openapi.json

OpenAPI 3.1 spec (plug into ChatGPT / Swagger)

GET

/health

Health check

GET

/books

List textbooks

GET

/books/{grade}/{subject}/topics

Chapter list

GET

/books/{grade}/{subject}/chapters/{n}

Full chapter text

GET

/books/{grade}/{subject}/chapters/{n}/metadata

Chapter metadata

GET

/search/content

Semantic search — returns highlight, actions, topic, bloom_level per result

GET

/search/chapters

BM25 keyword search

GET

/curriculum/{grade}/{subject}

Curriculum map (topics + Bloom's per chapter)

POST

/explain

Stream explanation (SSE) — Markdown, LaTeX, Mermaid diagram

POST

/question

Stream question generation (SSE)

GET

/exam-types

List supported exam types

POST

/question-paper

Generate full question paper

GET

/graph/prerequisites

Prerequisite topics

GET

/graph/learning-path

Full learning path

Quick start

Prerequisites

1. Clone and set up

git clone https://github.com/Jinansh230705/ncert-mcp
cd ncert-mcp
bun install

2. Configure environment

cp .env.example .env
# Edit .env — add OPENAI_API_KEY, optionally OPENAI_BASE_URL and MODEL_NAME

3. Ingest data (run once locally)

Downloads all K-12 NCERT PDFs, chunks them, tags each chunk with Bloom's level/topic/difficulty via OpenRouter, and writes the SQLite + Minisearch index:

bun run ingest

This is a one-time local step. Re-run to pick up newly added textbooks. Already-processed chapters are skipped automatically.

4. Run locally

# REST API + OpenAPI (http://localhost:8000)
bun run dev

# MCP stdio server (for Claude Desktop)
bun run dev:mcp

# MCP SSE server (for Claude Remote Connectors)
bun run dev:remote

Usage & Integrations

1. Hosted MCP (Claude.ai)

You can connect directly to the hosted MCP server without running it locally.

Connection Guide for Claude.ai:

  1. Go to Claude.ai and add a new Remote MCP Server.

  2. URL: https://ncert.getmaterio.app/mcp

  3. Authentication: None (No Auth)

2. ChatGPT Custom GPT

Try it out on ChatGPT using the GPT Store: NCERT GPT in ChatGPT's GPT Store

Alternatively, create your own Custom GPT and paste the OpenAPI spec: https://ncert.getmaterio.app/openapi.json

3. Local MCP — Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "ncert-mcp": {
      "command": "bun",
      "args": ["run", "/absolute/path/to/ncert-mcp/src/mcp.ts"]
    }
  }
}

4. Testing with MCP Inspector

npx @modelcontextprotocol/inspector bun run src/mcp.ts

5. Cloudflare Deployment

Deploy the repository to Cloudflare Workers using Wrangler:

bun run deploy

Note: Ensure you have provisioned a Cloudflare D1 database and updated your wrangler.toml accordingly. The database schema is automatically applied using the provided migrations or the ingestion script.

Contributing

Pull requests welcome!

  1. Add textbook mappings to NCERT_TEXTBOOK_CHAPTERS in src/pipeline.ts

  2. Run bun run ingest to download and index new PDFs

  3. Add new MCP tools to src/mcp.ts and src/mcp-express.ts

  4. Add corresponding REST routes to src/index.ts

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