Orinyx

Test and evaluate healthcare AI agents by deterministically verifying medication claims and orders against cited FDA drug labels.

Orinyx is a standardized Model Context Protocol (MCP) server and evaluation tool built to test clinical AI agents against cited FDA labels and coded answer keys. Operating completely deterministically with no LLM in the verdict logic, it parses medication orders into discrete claims—such as dosage, route, frequency, and contraindications—and validates them against FDA prescribing labels and OTC monographs. Designed strictly for synthetic test data in CI/CD pipelines and testing environments, Orinyx provides rule-derived severities, citation-backed results, and standardized exam suites to benchmark healthcare AI systems.

主要功能

01Pre-flight refusal mechanism to screen and block real patient identifiers from being transmitted
02Flexible deployment supporting hosted Streamable HTTP connections, local stdio, and CI command-line execution
031 GitHub stars
04Deterministic verification of raw or structured medication orders against cited FDA labels without LLM verdicts
05Built-in benchmark exams for scoring AI clinical tools against fixed synthetic case libraries
06Detailed claim-level analysis covering dose ranges, routes, frequencies, indications, and REMS attestations

使用案例

01Integrating automated medication safety and accuracy testing into CI/CD pipelines for clinical AI applications
02Evaluating and scoring healthcare conversational agents against synthetic benchmark exam sets
03Cross-referencing agent-generated clinical claims directly against official FDA prescribing labels and monographs