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DICOM, HL7v2, FHIR Interoperability MCP Server Introduced

DICOM/HL7/FHIR Interoperability MCP Server - Claude Code Marketplaces

June 3, 2026nyxtoolsdev

Summary

This MCP server, developed by a healthcare IT engineer, integrates three core medical standards—DICOM, HL7v2, and FHIR—into one package to achieve data interoperability. It is notable for addressing practical challenges such as PACS connectivity and cross-standard mapping.

Details

This server specializes in bridging the three core medical standards: DICOM, HL7v2, and FHIR. Specifically, it addresses complex real-world data integration challenges by providing PACS system connectivity (e.g., C-FIND/C-MOVE) and cross-standard mapping from DICOM tags to HL7v2, and subsequently to FHIR R4 resources. The available toolset includes not only simple tag lookups but also concrete conversion processes like 'DICOM→HL7' and 'HL7→FHIR,' Mirth Connect channel generation, and decoding of vendor-specific private tags. This achieves advanced interoperability by automatically mapping data retrieved from PACS into multiple standard formats (DICOM, HL7, FHIR) and attaching ORM messages. In the Japanese healthcare IT environment, where legacy systems (like HL7v2) coexist with modern API-based exchange models (FHIR), this type of 'bridging' functionality is critically important. This server holds high practical value by consolidating the knowledge and conversion logic between these disparate standards into a single platform. The toolset includes DICOM tag lookups, HL7 message parsing/validation, and even explaining integration patterns (like IHE SWF or ADT flows). This allows developers not only to send and receive data but also to build systems while understanding the underlying medical context and standardized workflow. The product offers a free version with reference tools only, and a premium version that includes PACS connectivity and advanced mapping/decoding features. The 'pacs_study_summary' endpoint—which retrieves data from PACS and automatically maps it to HL7/FHIR—significantly reduces the data preparation effort in real-world operations. (Note: Advertising content and irrelevant code snippets found in the source have been excluded.)

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