mcp-yolo

# MCP-YOLO [](https://pypi.org/project/mcp-yolo/) [](https://pepy.tech/project/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](file:///Users/rajanshukla/.gemini/antigravity/brain/6a2d32ac-d625-45bb-8f98-3d2916ab776e/pypi_setup_guide.md) for details.