1.6 KiB
Post 3 — Oct 14 · Rodado
Angle: AI agents over a real data platform; the hard part is the joins, not the data
In my first two posts I mapped every company in Switzerland and every .ch domain. This one goes back to where I come from: Brazilian public data.
Rodado is a mirror of 1,050 official Brazilian tables (238 datasets: employment records, death certificates, elections, public contracts, census) plus 77+ sources I scrape myself, like international sanctions lists and the ICIJ Offshore Leaks. All in Parquet, all queryable with DuckDB, and exposed as an MCP server so an AI agent can explore it directly.
Getting the data was the easy part. The hard part is that nothing has a foreign key. A municipality is id_municipio in one table, codIBGE in another, and sometimes arrives as a number with the leading zero gone. A column called valor appears in 91 tables and means something different in most of them; join on it and you get a big, plausible, wrong answer.
So the platform ships its own context for the agent: 54 bridges between equivalent columns, each with the SQL that converts one side into the other, plus single definitions for the recurring metrics. That context is what turns "an LLM with database access" into something that gives answers you can check.
The site also has 43 themes that read the tables side by side, on inequality, labour, health and power.
🔗 rodado.xyz
Before posting:
- Check the numbers against rodado.xyz on the day (tables, datasets, scraped sources, bridges)
- MCP access is restricted to selected members; say "access on request" in the comments if anyone asks