YOLO Computer Vision
The YOLO MCP Server enables AI assistants to perform computer vision tasks using state-of-the-art YOLO (You Only Look Once) models. Built with Python using FastMCP and Ultralytics, it provides tools for object detection, segmentation, classification, and pose estimation on images, as well as real-time camera analysis. The implementation offers both direct model integration and CLI-based approaches, supports model training and validation, and includes comprehensive image analysis that combines multiple model results. This server bridges the gap between AI assistants and computer vision capabilities, making it valuable for applications requiring visual understanding of user-provided images or camera feeds.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately.
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- media
- Source
- PulseMCP
- Repository
- github.com/gongrzhe/yolo-mcp-server
- Author type
- human
- Updated
- 2026-05-27
Use via MCP
Resolve YOLO Computer Vision 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.