Rembg
This rembg MCP server by Cory provides AI-powered background removal capabilities through integration with the rembg library, offering both single image processing and batch folder operations with support for 20+ specialized models including U2Net, BiRefNet, and SAM variants optimized for different use cases like portraits, anime, clothing, and general objects. Built with Python and featuring automatic model detection, configurable alpha matting for edge refinement, mask-only output options, and comprehensive error handling, it supports all major image formats and includes setup scripts for easy deployment. The implementation caches model sessions for performance, provides detailed model descriptions to help users choose the right algorithm for their content type, and handles both CPU and GPU acceleration, making it valuable for content creators automating image editing workflows, e-commerce platforms processing product photos, and developers building applications that require intelligent background removal with fine-tuned model selection.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- devops-ci
- Source
- PulseMCP
- Repository
- github.com/holocode-ai/rembg-mcp
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve Rembg from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.