National MCP Servers for Physical AI Oncology Clinical Trial Systems

Abstract

Prior oncology clinical trial infrastructure has been fragmented, site-specific, and inefficient across the critical dimensions of interoperability, auditability, privacy, and deployment. Each trial site has historically operated its own isolated systems for electronic health records, imaging archives, and audit logging, resulting in inconsistent data formats, duplicated regulatory effort, and limited cross-site collaboration. This paper presents the National MCP Physical AI Oncology Trials system, a proposed end-to-end architecture comprising five Model Context Protocol (MCP) servers that address these gaps through standardized, federated, and safety-governed capabilities. The five servers (Authorization, FHIR Clinical Data, DICOM Imaging, Audit Ledger, and Provenance) expose 23 tools across five hierarchical conformance levels, validated by 668 test functions. The system integrates AI and robotics advances intended to improve patient safety and clinical effectiveness, including emergency stop coordination, procedure state machines, and multi-party approval checkpoints. Quantitative analysis of the repository demonstrates 381 files across 88 directories, 34 integration adapters, 8 safety modules, 13 JSON schemas, and dual-language SDKs in Python and TypeScript. This end-to-end MCP Physical AI oncology trial system provides the foundation for national-scale standardization of robotic oncology clinical trials.

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