Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Open Census MCP ServerWhat's the median household income in Chicago compared to the national average?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Open Census MCP Server
Disclaimer
This is an independent, open-source experiment. It is not affiliated with, endorsed by, or sponsored by the U.S. Census Bureau or the Department of Commerce.
Data retrieved through this project remains subject to the terms of the original data providers (e.g., Census API Terms of Service).
What Is This?
An AI-powered statistical consultant for U.S. Census data. Ask questions in plain English, get accurate demographic data with proper statistical context, methodology guidance, and fitness-for-use caveats.
The insight: Census data has a pragmatics problem, not a search problem. Knowing WHICH data to use and HOW to interpret it matters more than finding it. This system encodes statistical consulting expertise into the AI interaction layer.
Status
🔬 Active Research & Rebuild — v3 architecture in progress. See docs/lessons_learned/ for the v1/v2 journey.
Vision
Census data influences billions in policy decisions, but accessing it effectively requires specialized knowledge. This project aims to make America's most valuable public dataset as easy to use as asking a question — with the statistical rigor of a professional consultant.
The opportunity: Every city council member, journalist, nonprofit director, and curious citizen should be able to fact-check claims and understand their communities with the same ease an eighth-grader uses a search engine. The data is public. The expertise to use it properly shouldn't be gatekept by technical complexity.
Architecture (v3)
Pure Python MCP server with pragmatic rules engine. No R dependency.
Pragmatic Rules Layer: Fitness-for-use constraints (MOE thresholds, coverage bias, temporal validity, source selection)
Census API Integration: Direct Python calls to Census Bureau APIs
Knowledge Base: Methodology documentation for RAG-enhanced guidance
Details: docs/architecture/ (coming soon)
Project Structure
Acknowledgments
U.S. Census Bureau — for collecting and maintaining vital public data
Kyle Walker — Analyzing US Census Data textbook as knowledge base source
Anthropic — Model Context Protocol enabling AI tool integration
Contributing
Contributions welcome, especially:
Domain expertise from Census data veterans
Statistical methodology review
Evaluation test cases (real-world query scenarios)
License
MIT License - see LICENSE file for details.