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io.github.rjn32s/mcp-yolo

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An MCP server providing zero-shot object detection and segmentation using Ultralytics YOLOE.

Source code Documentation

What can you do with io.github.rjn32s/mcp-yolo?

  • 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.

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

PYPI package mcp-yolo
Claude Code 
claude mcp add mcp-yolo -- uvx mcp-yolo==0.1.2
JSON configuration (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "mcp-yolo": {
            "command": "uvx",
            "args": [
                "mcp-yolo==0.1.2"
            ]
        }
    }
}

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. MCP-YOLO
  2. Key Features
  3. YOLOE Performance Reference
  4. Quick Start
  5. MCP Tools
  6. Publishing

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.

ModelBased OnmAP (COCO)Speed (T4/ms)Params (M)
YOLOE26-NYOLO26-N40.91.7~3.0
YOLOE26-SYOLO26-S48.62.5~10.0
YOLOE26-LYOLO26-L55.06.2~40.0
YOLOE-LYOLO11-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.

Frequently asked questions

What is io.github.rjn32s/mcp-yolo?

An MCP server providing zero-shot object detection and segmentation using Ultralytics YOLOE.

How do I install io.github.rjn32s/mcp-yolo?

It runs on your machine. In Claude Code: claude mcp add mcp-yolo -- uvx mcp-yolo==0.1.2. For other clients, paste the JSON configuration from this page into their MCP settings.

Does io.github.rjn32s/mcp-yolo need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is io.github.rjn32s/mcp-yolo open source?

Its code is public on GitHub, but the repository does not declare a standard license.

Who maintains io.github.rjn32s/mcp-yolo?

It is published by rjn32s. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.