mcp-yolo 0.1.2

MCP server for YOLOE zero-shot object detection and segmentation

MCP-YOLO

mcp-name: io.github.rjn32s/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.

Model	Based On	mAP (COCO)	Speed (T4/ms)	Params (M)
YOLOE26-N	YOLO26-N	40.9	1.7	~3.0
YOLOE26-S	YOLO26-S	48.6	2.5	~10.0
YOLOE26-L	YOLO26-L	55.0	6.2	~40.0
YOLOE-L	YOLO11-L	~52.0	~5.0	~26.0

Note: Performance varies depending on the hardware and input resolution. mcp-yolo uses yoloe-26l-seg.pt by default for high precision.

Quick Start

Installation

uv pip install mcp-yolo

Running the Server

uv run mcp-yolo

MCP Tools

detect_objects

Performs zero-shot detection.

• Arguments:
  • image_source (str): Path, URL, or Base64.
  • classes (list[str], optional): Custom text prompts to detect.

segment_objects

Performs zero-shot instance segmentation.

• Arguments:
  • image_source (str): Path, URL, or Base64.
  • classes (list[str], optional): Custom text prompts to segment.

Publishing

This project is configured for automated PyPI publishing. See the pypi_setup_guide.md for details.