Rag Node

MCP (Model Context Protocol) server for RAG (Retrieval-Augmented Generation) using Pinecone, OpenAI-compatible embedding APIs, and the official MCP SDK. Save documents and search by semantic similarity via MCP tools.

Add to MCP clients

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "rag": {
      "command": "npx",
      "args": ["-y", "@laskarks/mcp-rag-node"],
      "env": {
        "APIKEY": "sk-...",
        "EMBEDDING_MODEL": "text-embedding-3-small",
        "RAG_CHUNK_MAX_TOKENS": 1536,
        "PINECONE_API_KEY": "...",
        "PINECONE_INDEX": "rag-index",
        "PROVIDER": "openai | openrouter"
      }
    }
  }
}

Cursor (.cursor/mcp.json or MCP settings):

{
  "mcpServers": {
    "rag": {
      "command": "npx",
      "args": ["-y", "@laskarks/mcp-rag-node"],
      "env": {
        "APIKEY": "sk-...",
        "EMBEDDING_MODEL": "text-embedding-3-small",
        "RAG_CHUNK_MAX_TOKENS": 1536,
        "PINECONE_API_KEY": "...",
        "PINECONE_INDEX": "rag-index",
        "PROVIDER": "openai | openrouter"
      }
    }
  }
}

Tools

Tool	Description
save_to_rag	Chunk text, create embeddings, and save to Pinecone.
search_document_on_rag	Search documents by keyword using semantic similarity.

Installation

npm i @laskarks/mcp-rag-node

Environment Variables

Required

Variable	Description	Example
APIKEY	OpenAI or OpenRouter API key for embeddings	sk-...
EMBEDDING_MODEL	Embedding model ID	text-embedding-3-small, openai/text-embedding-3-small
PINECONE_API_KEY	Pinecone API key	...
PINECONE_INDEX	Pinecone index name (dimension must match embedding model)	rag-index
PROVIDER	AI provider (allowed values: openai, openrouter)	openai or openrouter

Important: Create your Pinecone index with the same dimension as your embedding model.

Embedding models and vector dimensions

Use the Dimension column when creating your Pinecone index.

Model	Dimension	Provider
text-embedding-3-small	1536	OpenAI, OpenRouter
text-embedding-3-large	3072	OpenAI, OpenRouter
text-embedding-ada-002	1536	OpenAI, OpenRouter
text-embedding-3-small (with dimensions param)	512–1536	OpenAI
voyage-3	1024	Voyage (via OpenRouter)
nomic-embed-text-v1.5	768	Nomic (via OpenRouter)
mistral-embed	1024	Mistral (via OpenRouter)
cohere/embed-english-v3.0	1024	Cohere (via OpenRouter)

For OpenRouter, use the model ID format, e.g. openai/text-embedding-3-small or voyage/voyage-3.

Optional

Variable	Description	Default
RAG_CHUNK_MAX_TOKENS	Max tokens per chunk before embedding	1536
RAG_CHUNK_OVERLAP	Overlap tokens between chunks	50

Usage

Run the server

npm run build
npm start

Or with env file:

# .env
APIKEY=sk-...
EMBEDDING_MODEL=text-embedding-3-small
PINECONE_API_KEY=...
PINECONE_INDEX=rag-index
PROVIDER=openai

npm start

Development

# Install dependencies
npm install

# Build
npm run build

# Run server (from compiled JS)
npm start

# Run server (dev, from TypeScript)
npm run dev

# Run sample client
npm run client

Project structure

src/
├── index.ts    # MCP server entry, tools registration
├── ai.ts       # AI controller (chunking, embeddings, Pinecone)
├── env.ts      # Environment loading
└── client.ts   # Example MCP client for testing
dist/           # Compiled output (after npm run build)

Publish to npm

Before publishing:

1. Add files to package.json to include only dist/ and docs:

 "files": ["dist", "README.md"]

2. Ensure npm run build succeeds and dist/ is committed or built on publish.
3. Add bin entry for npx rag-mcp-nodejs (optional):

 "bin": { "rag-mcp-nodejs": "dist/index.js" }

Note: MCP servers are usually run via node dist/index.js; a bin is optional. 4. Set a unique package name (npm may require scoped name, e.g. @yourname/rag-mcp-nodejs). 5. Add repository, homepage, and engines.node in package.json (optional but recommended).

Requirements

• Node.js >= 18
• Pinecone account
• OpenAI or OpenRouter API key

License

ISC