MCP Yolo

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "mcp-yolo": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-yolo"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

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

bash

uv pip install mcp-yolo

Running the Server

bash

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.

Read the full README →View source on GitHub →

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Frequently Asked Questions about MCP Yolo

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "mcp-yolo": { "command": "npx", "args": ["-y", "mcp-yolo"] } }

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Technical Specs & Signals

CategoryDeveloper Tools

27Quality signal: Emerging · 27/100How this signal is calculated ▾

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Documentation & tools11/30

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More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to MCP Yolo →Install in Claude Desktop Install in Cursor Install in VS Code

Documentation Overview

MCP-YOLO

mcp-name: io.github.rjn32s/mcp-yolo

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

bash

uv pip install mcp-yolo

Running the Server

bash

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.