MCP RAG Server

A Model Context Protocol (MCP) server that provides RAG (Retrieval-Augmented Generation) functionality using local embeddings via Ollama and Chroma vector database.

Presentation link

Features

• Local Processing: No external API costs - runs entirely locally

• Multiple Formats: Supports PDF, Markdown, and TXT files

• Smart Chunking: Configurable chunk size with overlap for better context

• Vector Search: Semantic search using nomic-embed-text model via Ollama

• MCP Integration: Works seamlessly with Cursor and other MCP clients

Related MCP server: MCP RAG with ChromaDB

Prerequisites

• Node.js (v18 or higher)

• Docker (for ChromaDB)

• Homebrew (for Ollama on macOS)

🚀 Quick Start

Setup (one time)

npm run setup

This will:

• Start Ollama and install nomic-embed-text model

• Start ChromaDB with Docker

• Build the project

• Ingest documents from ./docs

Development

# Start MCP server
npm run dev

# Ingest new documents
npm run ingest

Stop Services

npm run stop

Configuration

The server uses a config.json file for configuration:

{
  "documentsPath": "./docs",
  "chunkSize": 1000,
  "chunkOverlap": 200,
  "ollamaUrl": "http://localhost:11434",
  "embeddingModel": "nomic-embed-text",
  "chromaUrl": "http://localhost:8001",
  "collectionName": "rag_documents",
  "mcpServer": {
    "name": "mcp-rag-server",
    "version": "1.0.0"
  }
}

MCP Tools

• ingest_docs({path?}) - Ingest documents from a directory

• search({query, k?}) - Search for relevant document chunks

• get_chunk({id}) - Retrieve a specific chunk by ID

• refresh_index() - Clear and refresh the entire index

MCP Resources

• rag://collection/summary - Collection statistics and metadata

• rag://doc/<filename>#<chunk_id> - Individual document chunks

Configure in Cursor

Add to your Cursor MCP settings:

{
  "mcpServers": {
    "rag-server": {
      "command": "node",
      "args": ["/Users/luizsoares/Documents/buildaz/mcp_rag/dist/index.js"],
      "env": {}
    }
  }
}

Available Scripts

• npm run setup - Complete setup (Ollama + ChromaDB + build + ingest)

• npm run dev - Start MCP server in development mode

• npm run ingest - Ingest documents

• npm run build - Build the project

• npm run test - Run tests

• npm run stop - Stop all services

Troubleshooting

1. Ollama Connection Issues: Ensure Ollama is running on the configured URL

2. Model Not Found: Run ollama pull nomic-embed-text to install the embedding model

3. Docker Issues: Ensure Docker is running and accessible