How I Found a Great Anniversary Dinner Recommendation Using Aiven's MCP, PostgreSQL, and PG Studio

A food enthusiast, in search of an unforgettable anniversary dinner, decided to leverage Aiven for PostgreSQL and AI tools to create a personalized database of local restaurants, inspired by the MICHELIN Guide and a colleague's blog post. By storing the data in PostgreSQL and using postGIS for spatial queries, they were able to filter restaurants by distance and other attributes. The process involved using Aiven MCP and Claude Code for data importation, with AI assisting in schema development and upload scripts. Despite some challenges with older tool versions, prompt hygiene helped mitigate issues. The setup included installing Aiven MCP for Claude Code and updating preferences in the claude.md file to streamline the process. After successfully uploading and normalizing the data, the user explored it with PG Studio, verifying schema accuracy and running complex queries with postGIS to narrow down restaurant choices within 100 miles of Atlanta. The final step was creating a materialized view to facilitate future queries for dining recommendations, resulting in a reusable tool that cost under $5 and took less than an hour to build. The experience was shared as a guide for others to replicate, inviting them to explore and expand upon this project using Aiven for PostgreSQL.

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