No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by maorcc · MCP Server · ★ 173
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is gimp-mcp safe to install? View the security audit →
GIMP MCP Demo Full demo (with audio): https://github.com/maorcc/gimp-mcp/raw/main/docs/demo.mp4 AI agent using GIMP MCP to remove a background, edit a character's expression, and verify results — all through natural language via Claude Overview GIMP MCP bridges GIMP's professional image editing capabilities with AI assistants through the Model Context Protocol. It lets you edit images by describing what you want — and gives the AI a live visual feedback channel to verify each change before moving on. What makes it different from other GIMP integrations: The AI can see the image at any point in the workflow without saving to disk () Supports fully autonomous multi-step pipelines: open → edit → verify → refine → export 56 dedicated tool commands covering every m
| Stars | 173 |
| Forks | 25 |
| Language | Python |
| Category | MCP Server |
| License | GPL-3.0 |
| Quality Score | 68.4403511784195/100 |
| Open Issues | 2 |
| Last Updated | 2026-05-29 |
| Created | 2025-07-04 |
| Platforms | claude-code, cli, gemini, mcp, python |
| Est. Tokens | ~495k |
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gimp-mcp is GIMP MCP server. It is categorized as a MCP Server with 173 GitHub stars.
gimp-mcp is primarily written in Python. It covers topics such as claude-desktop, gemini-cli, gimp.
You can find installation instructions and usage details in the gimp-mcp GitHub repository at github.com/maorcc/gimp-mcp. The project has 173 stars and 25 forks, indicating an active community.
gimp-mcp is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to gimp-mcp on Agent Skills Hub include memorix, wcgw, daymon. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: