# medimageparse-mcp

> medimageparse-mcp — anihitk07-medimageparse-mcp. Use this tool when you need to accurately segment biomedical images using natural language prompts, and leverage the power of foundation models to extract meaningful masks and statistics from 2D and 3D images. It solves problems in medical image analysis, such as tumor detection and organ segmentation, by providing a simple free-text interface for AI agents. Ideal for use cases where remote or local image processing is required, with support for both stdio and Azure API Management gateway inputs and outputs.

Canonical page: https://skillsregistry.net/skills/anihitk07-medimageparse-mcp  
JSON: https://api.skillsregistry.net/v1/skills/anihitk07-medimageparse-mcp

## Description

Enables AI agents to segment 2D and 3D biomedical images using Microsoft's MedImageParse foundation models via free-text prompts, returning masks and statistics. Supports both local stdio MCP server and a hosted Azure API Management gateway for remote agents like Microsoft Foundry.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-09-03

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/hdpmup1jjs)
- **Repository:** <https://github.com/anihitk07/medimageparse-mcp>

## 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": "anihitk07-medimageparse-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/anihitk07-medimageparse-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/anihitk07-medimageparse-mcp/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