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.

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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

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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)

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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

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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

• Bun (v1.0+)
• An OpenRouter API key (or any OpenAI-compatible key)

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

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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/sse
3. Authentication: None (No Auth)

2. ChatGPT Custom GPT

Try it out on ChatGPT using the GPT Store: NCERT GPT

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.

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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