Open Census MCP Server is a Python-based MCP server that acts as an AI-assisted statistical consultant for U.S. Census data. It integrates a pragmatic rules layer to enforce fitness-for-use constraints, direct Census Bureau API calls via Python, and a knowledge base containing methodology and documentation to guide responses. The server is designed to interpret natural language questions, map them to appropriate Census metrics, apply methodological context, and present results with caveats about data quality, temporal validity, and source considerations. You can expect built-in capabilities for data selection, MOE (margin of error) handling, and guidance on methodology when interpreting or presenting census-related findings.

To use the server, run the MCP process using the configured Python module. The server exposes an interface for querying demographic data and obtaining contextual guidance. Users can rely on the rules engine to filter results by validity windows or coverage constraints, while the knowledge base provides explanations of the chosen metrics and the methodology behind the recommendations. The tools available include: (1) direct integration with Census APIs for data retrieval, (2) a pragmatic rules layer to ensure reported figures meet quality criteria, and (3) a knowledge base that documents methodology and RAG-enhanced guidance to support better decision-making. Interactions are designed to feel like consulting with a statistical expert rather than raw data retrieval.

Prerequisites:

• Python 3.8+ (recommended 3.9+)
• Pip (comes with Python)
• Internet access to install dependencies and fetch data

Step-by-step installation:

1. Clone the repository: git clone https://github.com/your-org/brockwebb-open-census-mcp-server.git cd brockwebb-open-census-mcp-server

2. (Optional) Create and activate a virtual environment: python -m venv venv

   On Windows

   venv\Scripts\activate

   On macOS/Linux

   source venv/bin/activate

3. Install dependencies: pip install -r requirements.txt

4. Configure environment variables (see additional_notes for details).

5. Run the MCP server: python -m open_census_mcp_server

6. Optional: run tests or an evaluation harness if provided in tests/: pytest tests/

Environment and configuration tips:

• API access: If the Census API requires an API key or tokens, set them via environment variables (for example, CENSUS_API_KEY) or a configuration file as documented in the repository.
• MOE and validity: The pragmatic rules layer uses thresholds and temporal validity checks; adjust MOE_THRESHOLD and TEMPORAL_VALIDITY settings as needed for your analysis window.
• Rate limiting: Be mindful of Census API rate limits; consider caching results where appropriate to avoid repeated requests.
• Logging: Enable verbose logging during development to diagnose integration or rule evaluation issues. Configure LOG_LEVEL in your environment to control verbosity.
• Module name: If you rename the Python module or package, update the mcp_config accordingly (the example uses open_census_mcp_server).