Why Orinyx Started as an MCP
I started building Orinyx because I believed healthcare AI needs independent verification closer to the point of use. That hasn't changed. What changed is where I start. Orinyx is a clinical AI hallucination benchmarkin
I started building Orinyx because I believed healthcare AI needs independent verification closer to the point of use. That hasn't changed. What changed is where I start.
Orinyx is a clinical AI hallucination benchmarking and governance platform, and the Orinyx MCP is now live on GitHub and completely free. This post covers why I'm starting with developers instead of hospitals.
*Where the original plan stalled
*
The original plan was to put independent verification inside EHR workflows, which meant starting with healthcare executives, procurement, security reviews, and EHR integrations. Throughout the summer, the feedback I heard most was that healthcare is hard to get into. Nobody told me the idea was bad, and they were right about the access. For a solo founder, that path takes more hours than I have, and the bills still have to get paid.
I could have shelved it. Instead I asked who I can actually talk to, and the answer was the developers building these systems and running into the most problems with them.
*What developers are dealing with
*
Over the past few weeks I've been surveying developers about the pain points in their agentic development process. Between that feedback, my architecture work, and early testing, these problems keep coming up.
β’ Fragmented clinical terminology. Medication names, formulations, routes, and coding systems don't always map cleanly across data sources.
β’ Incomplete evaluation coverage. A model can pass simple tests and still fail on missing instructions, ambiguous orders, or multi-step scenarios.
β’ Weak evidence traceability. A pass or fail result doesn't tell you which reference supports the verdict.
β’ Inconsistent handling of uncertainty. Systems guess when they should abstain, or abstain without saying what's missing.
β’ Few realistic testing environments. Developers need to evaluate healthcare AI without live patient data or enterprise EHR access.
β’ Evaluator dependence. When closely related models generate and judge outputs, their errors can correlate, and a system checking itself isn't really a check.
β’ Regression testing. Changes to prompts, schemas, models, and clinical references introduce failures that earlier tests won't catch.
My own medication verification tests surfaced examples of these, including missing PRN daily totals, oral versus IV evidence mismatches, formulation ambiguity, and incomplete frequency interpretation. Those findings are shaping what I'm building next, which includes evidence mapping, frequency calculation, missing information handling, interaction checks, and independent judging. These are problems I'm building Orinyx to address, and they aren't all solved yet.
*Why an MCP
*
An MCP server lets developers reach verification capabilities from the AI development tools they already use, without a healthcare enterprise contract in the way. Open source matters here too. Orinyx asks developers to trust an independent verification system, and that trust comes from behavior you can inspect rather than from marketing claims.
Starting with developers also gives me a way to earn technical credibility, see how people actually use it, find failure patterns, and improve the engine before taking on enterprise relationships. Healthcare and life sciences are the focus, though evidence, traceability, regression testing, independent evaluation, and governance show up in other regulated industries too.
Where Orinyx is heading
β’ Free open source MCP. Synthetic data testing, basic verification, and shareable results. This is live now.
β’ Paid verification API. More advanced testing, evaluation, observability, evidence reporting, and integrations, starting with synthetic data.
β’ Enterprise API. Eventually supports authorized real patient data workflows, subject to security, compliance, contractual, and integration requirements.
The long-term goal is still verification inside clinical workflows, including EHR-connected environments, and the MCP gives me a more practical route there.
*Try it
*
The repo is here, https://github.com/lexthirteen-ai/orinyx-mcp
Orinyx started at Startup Weekend, where a team helped me in the early days, and I've built the MCP on my own since. I'm maintaining it as a solo founder, shipping updates every week, and I'd like your eyes on it. Try it, star it if it's useful, and tell me what breaks or what's missing.
If you're building healthcare AI agents, what's the hardest part of evaluating them before they reach a clinic?
Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes β full credit and traffic to the original publisher.