Rent Sniper

Inspiration

The rental market is broken. As someone who's spent countless hours scrolling through Zillow, Apartments.com, and Craigslist, I realized that finding the perfect rental property shouldn't feel like a full-time job. Traditional rental search platforms force users to manually filter through thousands of listings, often missing great opportunities or wasting time on properties that don't match their actual needs.

I was inspired to create an AI-powered rental assistant that could understand natural language queries, gather detailed preferences through conversation, and automatically scrape multiple rental platforms to find the perfect match. Instead of users hunting for properties, the properties would come to them.

What it does

Rent Sniper is an intelligent rental property search platform that combines conversational AI with real-time web scraping. Users simply chat with our AI agent in natural language about what they're looking for:

Conversational Search: "I need a 2-bedroom apartment in Seattle under $3000 with parking" Smart Criteria Gathering: The AI asks follow-up questions to understand budget, move-in dates, amenities, and preferences

Multi-Platform Scraping: Once criteria are complete, it automatically searches Zillow, Redfin, and other rental sites

Real-Time Results: Returns actual property listings with photos, prices, and contact information User Authentication: Secure login via Auth0 with personalized search history

How we built it Frontend Architecture: React with Vite for fast development and optimized builds TypeScript for type safety and better developer experience Tailwind CSS with shadcn/ui components for modern, responsive design Auth0 integration for secure user authentication Backend & AI Workflow: n8n Cloud for workflow automation and AI agent orchestration Anthropic's Claude AI model for natural language understanding Model Context Protocol (MCP) for connecting AI agents to external tools Apify actors for web scraping rental websites (Zillow, Redfin scrapers) Infrastructure: Vercel for frontend deployment with global CDN n8n Cloud for serverless workflow execution Webhook-based communication between frontend and AI workflows Challenges we ran into MCP Connection Issues: The biggest challenge was connecting the AI agent to Apify's web scraping tools via Model Context Protocol. We encountered persistent "Could not connect to your MCP server" errors that required deep debugging of authentication headers, endpoint configurations, and Apify server status. Authentication Flow: Implementing secure authentication between the React frontend, n8n workflows, and Apify services required careful handling of API tokens and webhook security. Data Structure Mapping: Transforming scraped rental data from different platforms (Zillow vs Redfin) into a consistent format for the frontend required extensive data normalization. Real-Time Communication: Ensuring smooth communication between the chat interface and long-running scraping workflows while maintaining good user experience with loading states and error handling. Accomplishments that we're proud of Successfully integrated AI agents with web scraping: Created a seamless flow from natural language input to actual rental listings Zero-configuration deployment: Achieved one-click deployment to Vercel with automatic builds and previews Conversational UX: Built an intuitive chat interface that feels natural and guides users effectively Multi-platform data aggregation: Successfully scrape and normalize data from multiple rental websites Scalable architecture: Designed a system that can easily add new rental platforms and AI capabilities What we learned AI Integration Complexity: Connecting AI agents to external tools (MCP) is still an emerging field with many rough edges. Documentation is sparse and debugging requires patience. Webhook Reliability: Building robust webhook-based communication requires careful error handling, timeouts, and retry logic. User Experience Design: Conversational interfaces need careful UX design to guide users without being overwhelming or confusing. Deployment Pipeline: Modern tools like Vercel make deployment incredibly smooth, but environment variable management across services requires attention. Data Quality: Web scraping rental sites requires constant monitoring as websites change their structure frequently.

Built With

• anthropic-claude
• frontend:-react
• model-context-protocol-(mcp)-web-scraping:-apify-(zillow-&-redfin-scrapers)-deployment:-vercel-apis:-webhook-based-communication
• rest
• shadcn/ui-authentication:-auth0-ai-&-automation:-n8n-cloud
• tailwind-css
• typescript
• vite