# DICOMweb MCP Server

> Use this tool when you need to integrate DICOM image archives with AI assistants, enabling natural language searches and interactions with medical imaging data. It solves problems of accessing and analyzing medical images, reports, and metadata, and provides outputs such as image frames and report data through a natural language interface. Ideal for use cases where AI-driven medical image analysis and reporting are required, such as clinical decision support and research applications.

Canonical page: https://skillsregistry.net/skills/pantelisgeorgiadis-dicomweb-mcp-server  
JSON: https://api.skillsregistry.net/v1/skills/pantelisgeorgiadis-dicomweb-mcp-server

## Description

An MCP server that exposes a DICOMweb-compliant DICOM archive to AI assistants. It lets any MCP-capable client search studies, series and instances, inspect metadata, read Structured and Encapsulated PDF Reports, and render image frames — all through natural language.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** media
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/z9bgn9jeic)
- **Repository:** <https://github.com/PantelisGeorgiadis/dicomweb-mcp-server>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "pantelisgeorgiadis-dicomweb-mcp-server"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/pantelisgeorgiadis-dicomweb-mcp-server` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/pantelisgeorgiadis-dicomweb-mcp-server/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt