# Photographi MCP

> Use this tool when you need to analyze and organize large photo libraries, as it solves problems related to image categorization, object detection, and visual search. The Photographi MCP engine takes in photo libraries as input and outputs categorized and tagged images, enabling efficient searching and management. Ideal for use cases where local computer vision capabilities are required, such as in applications with large image datasets or sensitive visual information.

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

## Description

Visual Intelligence Command Center: A Local Computer Vision Engine for Photo Libraries.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** media
- **Updated:** 2026-04-25

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/photographi-mcp)
- **Repository:** <https://github.com/prasadabhishek/photographi-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": "photographi-mcp"
    }
  }
}
```

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