Fluxinc DICOM MCP Server

The Fluxinc DICOM MCP Server is a lightweight Model Context Protocol (MCP) service that bridges AI assistants with DICOM-enabled imaging infrastructures. It provides a standardized API for querying and interacting with Picture Archiving and Communication Systems (PACS) without exposing the underlying network details to the client. By encapsulating DICOM operations behind MCP tools, developers can embed imaging diagnostics and validation directly into AI workflows, enabling automated quality checks, data integrity verification, and rapid prototyping of medical imaging pipelines.

What Problem Does It Solve?

In clinical environments, AI assistants often need to verify connectivity or retrieve imaging metadata from PACS. Traditional approaches require hard‑coded AE titles, IP addresses, and port numbers, which are error‑prone and difficult to maintain across multiple sites. The DICOM MCP Server introduces a declarative node configuration file () that abstracts these details. Developers can reference nodes by simple names, and the server resolves the full DICOM connection parameters internally. This eliminates configuration drift, reduces onboarding time for new sites, and protects sensitive network information from being exposed in code or prompts.

Core Functionality and Value

At its heart, the server offers a suite of DICOM tools exposed through MCP:

• – Enumerates all configured nodes, allowing AI assistants to present a concise menu of available imaging resources.
• – Performs a DICOM C‑ECHO (verification) against a named node, optionally selecting a local AE title from the configuration. This is essential for health‑checks and ensuring that the PACS is reachable before initiating larger data transfers.
• – Executes a C‑ECHO with explicit parameters, giving developers full control when dynamic or non‑standard connections are required.

These tools enable AI assistants to seamlessly integrate health‑check routines into diagnostic or triage workflows, automatically confirming that imaging systems are online before proceeding with image retrieval or analysis.

Key Features Explained

• Declarative Node Management: The file centralizes all DICOM node definitions, including AE titles, IPs, ports, and descriptive metadata. This file can be version‑controlled alongside application code, ensuring that any change to the imaging environment is tracked and auditable.
• Local AE Title Selection: Multiple local AE titles can be defined, allowing the server to act as different entities (e.g., a gateway or a direct PACS client) without modifying the code that invokes the MCP tools.
• Automatic Integration with Claude: When installed via , the service is automatically launched and managed by Claude, freeing developers from manual start‑up scripts and ensuring that the server is always available when the AI assistant needs it.
• Cross‑Platform Compatibility: Built on Python and leveraging for fast dependency resolution, the server runs on any system that supports these tools, from local development machines to cloud‑based AI runtimes.

Real‑World Use Cases

• Quality Assurance Pipelines: CI/CD workflows that deploy new AI models can include a pre‑deployment check using to confirm that the target PACS is reachable, preventing downstream failures.
• Dynamic Imaging Retrieval: An AI assistant can present a list of hospital nodes to a clinician, then perform a quick C‑ECHO before initiating a DICOM query or retrieval operation, ensuring that the selected node is operational.
• Multi‑Site Coordination: In research studies spanning several hospitals, the server’s node abstraction allows a single AI script to interact with all participating PACS without hard‑coding site‑specific details, simplifying collaboration and data federation.

Unique Advantages

Unlike generic DICOM libraries that expose raw networking APIs, the Fluxinc DICOM MCP Server encapsulates connection logic behind a clean, high‑level interface. This abstraction not only reduces boilerplate code but also enhances security by keeping sensitive network parameters out of prompts and logs. Its tight integration with MCP means that any AI assistant capable of speaking the protocol can immediately leverage these tools, making it a plug‑and‑play component for developers building imaging‑aware AI applications.