MCP RAG Server
Provides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.
README
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.
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
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 directorysearch({query, k?})- Search for relevant document chunksget_chunk({id})- Retrieve a specific chunk by IDrefresh_index()- Clear and refresh the entire index
MCP Resources
rag://collection/summary- Collection statistics and metadatarag://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 modenpm run ingest- Ingest documentsnpm run build- Build the projectnpm run test- Run testsnpm run stop- Stop all services
Troubleshooting
- Ollama Connection Issues: Ensure Ollama is running on the configured URL
- Model Not Found: Run
ollama pull nomic-embed-textto install the embedding model - Docker Issues: Ensure Docker is running and accessible
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