Tech sailorsSubmitted July 26, 2026

Lab Report Triage Assistant

An MCP app on the Model Context Protocol built by Tech sailors at the Amrita University Coimbatore NitroStack × MCP To The Moon hackathon and deployed on NitroStack.

About this project

Our system provides an end-to-end AI-powered healthcare workflow that begins by accepting a patient's laboratory report in the form of text, image, or PDF. Image and PDF reports are first processed through OCR or PDF parsing to extract raw text, which is then standardized into structured laboratory parameters by the parse_labs module. The extracted values are analyzed by flag_critical to classify each test as Normal, Borderline, or Critical using reference ranges. Based on the analysis, suggest_doctor recommends the most appropriate specialist while considering whether the patient should continue with an existing doctor or consult a new one. The patient can then view available appointment slots, book a consultation, and receive a confirmation with intake details. Finally, share_summary generates a concise, patient-friendly summary that can be shared via messaging platforms. For a seamless experience, run_full_triage automates the complete workflow in a single call while maintaining patient history and trend analysis across multiple visits, and secure authentication ensures role-based access for both patients and doctors.

Team Tech sailors

  • Vani shree SLead

  • Shivanandana A

  • PRAJIT T

  • Dhakshin N

Frequently asked questions

What does Lab Report Triage Assistant do?
Our system provides an end-to-end AI-powered healthcare workflow that begins by accepting a patient's laboratory report in the form of text, image, or PDF. Image and PDF reports are first processed through OCR or PDF parsing to extract raw text, which is then standardized into structured laboratory parameters by the parse_labs module. The extracted values are analyzed by flag_critical to classify each test as Normal, Borderline, or Critical using reference ranges. Based on the analysis, suggest_doctor recommends the most appropriate specialist while considering whether the patient should continue with an existing doctor or consult a new one. The patient can then view available appointment slots, book a consultation, and receive a confirmation with intake details. Finally, share_summary generates a concise, patient-friendly summary that can be shared via messaging platforms. For a seamless experience, run_full_triage automates the complete workflow in a single call while maintaining patient history and trend analysis across multiple visits, and secure authentication ensures role-based access for both patients and doctors.
Who built Lab Report Triage Assistant?
Lab Report Triage Assistant was built by team Tech sailors at the Amrita University Coimbatore NitroStack × MCP To The Moon hackathon.
What is an MCP app and how is it built?
An MCP app is an application built on the Model Context Protocol — an open standard that lets AI agents connect to tools, data, and APIs. This project exposes MCP tools and resources that agentic AI systems can call. It was built and deployed on NitroStack, the full-stack platform for shipping MCP apps and servers.