A Rails 8 MCP (Model Context Protocol) server that provides a knowledge base for AI agents. Elevate your AI applications with enhanced reasoning and dynamic tool usage through RAG-powered document retrieval.

• Document Management - Create and organize markdown documents via web UI
• PDF Processing - Upload PDFs with automatic text extraction
• Website Scraping - Import content from web pages and crawl entire sites
• RAG Search - Vector similarity search powered by pgvector embeddings
• MCP Interface - Connect directly to Claude, Cursor, or any MCP-compatible AI agent

• Ruby 3.4.6
• PostgreSQL 15+ with pgvector extension
• Ollama (for local embeddings)

# Clone the repository
git clone https://github.com/your-org/minerva.git
cd minerva

# Install dependencies
bundle install

# Set up environment variables (optional, defaults work for local development)
export POSTGRES_USER=your_user
export POSTGRES_PASSWORD=your_password
export POSTGRES_HOST=127.0.0.1

# Set up database with pgvector
bin/rails db:prepare

# Pull the embedding model
ollama pull nomic-embed-text:v1.5

# Start the server
bin/dev

The web interface will be available at http://localhost:3000.

Minerva exposes an MCP endpoint at POST /mcp. Configure your AI agent to connect:

Add to your ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "minerva": {
      "url": "http://localhost:3000/mcp"
    }
  }
}

Add to your MCP settings:

{
  "minerva": {
    "url": "http://localhost:3000/mcp"
  }
}

1. Add Documents - Use the web UI to create markdown documents or upload PDFs
2. Scrape Websites - Add site URLs to automatically import their content
3. Organize - Use categories to organize your knowledge base
4. Query - Your AI agent can now search and retrieve relevant documents via the DocumentSearch tool

bin/dev                          # Start development server
bin/rails test                   # Run tests
bin/rubocop                      # Lint Ruby code
bin/brakeman --no-pager          # Security scan

• PostgreSQL with pgvector for vector storage (768-dim embeddings, HNSW index)
• Solid Queue/Cache/Cable for background jobs and caching (no Redis required)
• Propshaft + importmap-rails for assets (no Node.js required)
• Kamal for deployment

• Mario Alvarez
• Jorge Alvarez

MIT