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licenseA
qualityA
maintenanceMCP server enabling a florr.io game agent with YOLO vision, entity prediction, combat assessment, and local knowledge base for autonomous gameplay.
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MIT
Best YOLO MCP Servers
Ultralytics YOLO is an open-source computer vision model family known for real-time object detection, image/video segmentation, and classification.
Why this server?
Uses YOLO for real-time visual perception to detect and track game entities, providing the visual input for the agent's decision-making.
Why this server?
Provides access to YOLO plugins through AScript's online plugin library for computer vision and object detection capabilities in device automation workflows.
AlicenseAqualityCmaintenanceEnables AI programming tools to query AScript API documentation and control real Android/iOS devices for automation scripting, including device interaction, development deployment, and file management.298MITWhy this server?
Supports YOLOv5 object detection models for real-time AI inference with accelerators like MemryX MX3 and Coral TPU, achieving 80+ FPS on Raspberry Pi.
AlicenseBqualityBmaintenanceEnables AI assistants to manage homelab infrastructure through automated service installation (Jellyfin, Pi-hole, Ollama, Home Assistant, Frigate NVR), VM operations, AI accelerator support (MemryX, Coral TPU, Hailo-8), and Terraform state management with SSH-based discovery and deployment.585MITWhy this server?
Recommends using YOLO mode for optimal experience, suggesting the MCP server works best with this feature enabled in Cursor settings.
AlicenseBqualityDmaintenanceAn MCP server providing web development tools such as screen capturing capabilities that let AI agents take and work with screenshots of the user's screen.234 npm15MITWhy this server?
Provides optional computer-vision object detection using a local YOLO model, adding a second opinion to the robot's own AI detections for balls, barrels, robots, and AprilTags.
AlicenseAqualityBmaintenanceEnables AI assistants to drive and control a VEX AIM coding robot over Wi-Fi, using its camera, lights, screen, speaker and kicker to navigate, detect objects, and play soccer. Also serves a live browser control panel for driving, team setup, and running autonomous-then-driver matches, with simulators available for testing without hardware.40MITWhy this server?
Exports an image's polygons and circles as YOLO label files and class lists, and merges multiple images into one YOLO dataset with a single consistent class ordering.
AlicenseAqualityAmaintenanceEnables AI assistants such as Claude to list and upload images on an image annotation platform, add, replace, or remove pins, polygons, circles, rectangles, and arrows, edit image metadata, and export annotations as COCO JSON or YOLO training data. It can run over stdio or Streamable HTTP so multiple clients can share one server, with an included LangGraph terminal agent.221,259 PyPIMITWhy this server?
Recommends using YOLO mode in Cursor settings for optimal experience with the frontend review tool.
AlicenseAqualityDmaintenanceAn MCP server that reviews UI edit requests by comparing before and after screenshots, providing visual feedback on whether changes satisfy the user's requirements.171 npm15GPL 2.0Why this server?
Recommends using YOLO mode in Cursor settings to optimize the frontend review experience, allowing for more seamless interaction with the visual review tools.
FlicenseBqualityNot gradedmaintenanceAn MCP server that visually reviews UI edit requests by comparing screenshots before and after edits, ensuring changes satisfy user requests.171 npm31-Why this server?
Allows for the configuration, labeling, and training of custom YOLOv8 object detection models through an automated MCP workflow.
SensorMCP Serverofficial
AlicenseNot gradedqualityDmaintenanceAn MCP server that enables automated dataset creation and custom object detection model training through natural language interactions. It integrates foundation models like GroundedSAM for auto-labeling and supports training specialized YOLOv8 models using local or Unsplash images.4MIT