Mcp Yolo
Wraps Ultralytics YOLOE to expose zero-shot object detection and segmentation through two MCP tools. You pass an image as a local path, URL, or Base64 string, optionally specify text prompts like "blue coffee cup next to the spoon", and get back bounding boxes or polygon masks without training a model. Uses yoloe-26l-seg.pt by default, which hits 55.0 mAP on COCO at 6.2ms on T4. Reach for this when you need Claude or another agent to parse objects from images using arbitrary natural language queries instead of fixed class lists.
- GitHub stars
- 1
- tools
- 2
- transport
- STDIO
- last updated
- Feb 25, 2026
Tools
2 toolsPublic tool metadata for what this MCP can expose to an agent.
detect_objectsPerforms zero-shot detection.
Performs zero-shot detection.
No parameter schema in public metadata yet.
segment_objectsPerforms zero-shot instance segmentation.
Performs zero-shot instance segmentation.
No parameter schema in public metadata yet.
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MCP-YOLO
MCP-YOLO is an agent-first development platform that provides Zero-Shot Object Detection and Segmentation as a Model Context Protocol (MCP) server. Powered by Ultralytics YOLOE, it enables developers and AI agents to detect and segment objects using arbitrary text prompts without retraining.
Key Features
- Zero-Shot Detection: Detect any object using natural language (e.g., "the blue coffee cup next to the spoon").
- Instance Segmentation: Precise polygon masks for discovered objects.
- Flexible Image Inputs: Supports local file paths, remote URLs, and Base64 encoded strings.
- Agent Optimized: Includes custom "Skills" for autonomous deployment and benchmarking.
YOLOE Performance Reference
YOLOE builds upon the latest YOLO architectures (like YOLO11 and YOLO26) to provide state-of-the-art open-vocabulary performance.
Details
- Repository
- github.com/rjn32s/mcp-yolo
- Package
mcp-yolo- Transport
- STDIO
- Registry
- MCP registry, active