DICOM MCP Server for Medical Imaging Systems

DICOM MCP Server for Medical Imaging Systems 🏥

The dicom‑mcp server bridges the gap between AI assistants and medical imaging repositories. It exposes a set of high‑level tools that let language models query patient metadata, retrieve embedded PDF reports from DICOM instances, and forward image series to external processing nodes such as segmentation or classification services. By encapsulating the complexity of DICOM networking—C‑Query, C‑Move, and C‑Get operations—the server allows developers to focus on building conversational workflows rather than low‑level protocol handling.

Problem it solves

Clinical decision support often requires rapid access to a patient’s imaging history and associated reports. Traditional PACS systems expose data through proprietary interfaces or require custom integration code, which is time‑consuming and error‑prone. The dicom‑mcp server provides a standardized, MCP‑compatible API that any LLM can call to perform common imaging tasks. This eliminates the need for bespoke connectors and ensures consistent behavior across different DICOM backends (PACS, VNA, or local servers like Orthanc).

What the server does

• Metadata discovery: Search for patients, studies, series, and instances using flexible criteria (patient name, study date, modality).
• Report extraction: Pull DICOM instances that contain encapsulated PDF reports and decode the text, enabling LLMs to read and summarize clinical findings.
• Image forwarding: Send selected series or entire studies to another DICOM node, typically an AI inference endpoint that can return segmentation masks, volume measurements, or other analytics.
• Connection management: Configure multiple DICOM nodes and AE titles via a YAML file, allowing the server to act as a gateway for diverse imaging environments.

Key features

• Unified toolset: A small, focused set of tools that cover the most common imaging interactions.
• MCP‑ready: Exposes resources, tools, and prompts in a format that Claude or other LLMs can consume directly.
• Extensible configuration: YAML‑based node definitions let developers add or swap servers without code changes.
• Safety warnings: Built‑in cautions remind users that the server is not for production clinical use, protecting patient data.

Real‑world scenarios

• Radiology triage: A clinician asks an AI assistant to pull the latest CT report for a patient, and the server returns the text in seconds.
• Volume tracking: The assistant queries historical scans, forwards them to a segmentation node, and reports organ volumes over time—useful for oncology follow‑up.
• Rapid research: A researcher can script queries to pull imaging cohorts and associated reports for training a new AI model, all through MCP calls.

Integration with AI workflows

Once registered in the client’s configuration, the dicom‑mcp server becomes a first‑class tool for any LLM. The model can issue high‑level commands like or , and the server translates those into proper DICOM operations. The response payloads are then parsed by the LLM, allowing it to generate natural‑language summaries or actionable insights. Because all interactions are mediated by MCP, the same server can serve multiple assistants (Claude, GPT‑4o, etc.) without modification.

Unique advantages

• Zero DICOM expertise required: Developers can invoke complex DICOM actions with simple function calls.
• Privacy‑first design: Intended for local or sandbox environments, ensuring that sensitive imaging data never leaves the controlled network.
• Modular architecture: The toolset can be extended with additional DICOM operations or wrapped around other imaging services, making it a flexible core for AI‑driven medical applications.

Overall, the dicom‑mcp server turns a legacy imaging archive into an AI‑friendly data lake, enabling conversational agents to access, analyze, and act on patient imaging information with minimal friction.