Building an HS Code Commodity Search Engine with Algolia and Claude

#algoliachallenge #webdev #devchallenge #ai

Algolia MCP Server Challenge: Backend Data Optimization

This is a submission for the Algolia MCP Server Challenge

What I Built

I developed an HS Code Commodity Search Engine for the Algolia MCP Server Challenge. This web app enables users to search Harmonized System (HS) codes, filter by categories and tariffs, and analyze tariff differences using Anthropic’s Claude AI. Hosted with a Node.js backend on Heroku and a React frontend on Vercel, it targets trade-related needs.

Benefit for Users:

For exporters, tariff analysis offers critical insights before exporting, aiding decision-making.
For government users, Algolia provides a searchable, public-facing tariff database, simplifying commodity information access.

Key Project Features

Search: Algolia powers fast HS code and commodity lookups.
Filters: Category and tariff filters refine results for a tailored experience.
Tariff Analysis: Claude AI delivers real-time tariff difference insights.

Demo

Explore the live demo at Vercel URL and YouTube. Search "horses," filter by "Live Animals," and click “Analyze Tariffs with Claude” to see the analysis.

Repo

The code is on GitHub under the MIT License, open for collaboration.

How I Utilized the Algolia MCP Server

I used the Algolia MCP Server to index a dataset of 39 dummy commodities with HS codes, tariffs for Indonesia and the USA, and categories. The backend leverages Algolia’s search API for efficient queries and facet-based filtering, demonstrating robust platform utilization.

Innovative Approach to Algolia Usage

The innovation combines Algolia’s search with Claude AI for tariff analysis. While Algolia retrieves data, Claude processes it for insights, enhanced by planned synonym support (e.g., "timber," "lumber" for "wood") to boost relevance.

How It Worked Step by Step

Data Preparation: Parsed a CSV with dummy HS code data.
Algolia Indexing: Indexed data with facets for filters.
Backend Setup: Built a Node.js server with Express for search/enrich endpoints.
AI Integration: Integrated Claude API for analysis.
Frontend Development: Developed a React app with Tailwind CSS, linking to the backend.
Deployment: Deployed on Heroku and Vercel, ensuring port compatibility.

Key Challenges

CSV Parsing Issues: Handled malformed dummy data with validation.
Duplicate Records: Added deduplication logic in Algolia.
Facet Configuration: Iterated to ensure accurate filtering.

Key Takeaways

Algolia’s speed and facets transform data-driven apps.
Claude integration adds value but requires API optimization.
Heroku/Vercel deployment was streamlined, though port setup was tricky.

Next Development and Current Limitations

Current Limitations: The app uses dummy data comparing Indonesia and USA tariffs, covering only 39 commodities—not all HS codes.
Next Steps: Expand to other countries with multilingual support (e.g., Spanish, Mandarin), automate tariff updates with n8n, and configure Algolia to recognize synonyms like "timber" and "lumber" as related to "wood" for better relevance.

Conclusion

Thanks to the DEV Community and Algolia MCP Server Challenge team! The project is open-source under the MIT License—contribute on GitHub. Test it at Vercel URL!