This Model Context Protocol (MCP) server provides comprehensive intelligence on elder care facilities, enabling AI clients to quickly assess nursing homes, assisted living, or memory care facilities. It orchestrates nine parallel data sources, including OSHA inspections, CFPB complaints, multi-platform review scrapers, corporate registries, and Google Maps, to produce composite safety scores, complaint pattern analyses, ownership transparency ratings, quality composites, and staff adequacy estimates. Designed for applications like private equity due diligence, insurance underwriting, family placement decisions, and regulatory oversight, the server automates manual data collection and synthesis, delivering structured JSON reports with 0-100 scores and actionable signals in under 30 seconds, all without requiring users to manage API keys or infrastructure.
주요 기능
014 scoring models + composite verdict for a single 0-100 composite risk score with categorical verdicts
02Parallel actor dispatch, reducing 9-source composite assessments to the latency of the slowest single source
03OSHA violation classification, differentiating serious, willful, and repeat violations with weighted penalties
04CFPB complaint severity triage and cross-platform complaint correlation to flag systemic issues
05Staffing keyword NLP on reviews and complaints, cross-referenced with OSHA violations for adequacy estimates
060 GitHub stars
사용 사례
01Private equity acquisition due diligence for surfacing compliance risk and ranking acquisition candidates
02Insurance risk underwriting for objective safety and staffing indicators beyond loss run history
03Family placement decisions for comprehensive, unbiased information on elder care facilities
04Portfolio risk monitoring to track safety scores, complaint volumes, and quality ratings over time
