Screenshot Capture With Region Selection

via “screenshot-and-screen-capture-with-element-highlighting”

I've been building computer-use tools for a while, and I quietly launched this about a month ago (122 Stars on GH). I figured it was worth sharing here.Over the last few months, a lot of computer-use agents have come out: Codex, Claude Code, CUA, and others. Most of them seem to work roughly li

Unique: Combines raw screenshot capture with accessibility tree data to overlay semantic element information (bounding boxes, labels) rather than relying on OCR or image analysis — provides agents with both visual and structural context

vs others: More accurate element highlighting than vision-based approaches because it uses accessibility metadata, but requires that elements are properly exposed in the accessibility tree

via “screenshot capture and visual state recording”

** (by UI-TARS) - A fast, lightweight MCP server that empowers LLMs with browser automation via Puppeteer’s structured accessibility data, featuring optional vision mode for complex visual understanding and flexible, cross-platform configuration.

Unique: Integrates screenshot capture as a native MCP tool with configurable formats and element-specific clipping, enabling vision models to receive targeted visual input rather than full-page screenshots, reducing token consumption and improving analysis focus

vs others: Native integration vs external screenshot tools; supports element-specific clipping for vision model efficiency; full-page capture capability beyond viewport limitations of basic screenshot tools

via “screenshot-and-visual-capture”

Experimental MCP server for browser automation using Puppeteer (inspired by @modelcontextprotocol/server-puppeteer)

Unique: Integrates Puppeteer screenshot capability into MCP, allowing agents to request visual snapshots as part of automation workflows. Supports both full-page and region-specific captures with configurable output formats.

vs others: More flexible than static screenshot tools; agents can request screenshots at any point in a workflow to verify state or debug failures

via “window-specific screenshot targeting”

** - Privacy-first macOS MCP server that provides visual context for AI agents through window screenshots

Unique: Implements window enumeration and filtering to allow agents to target specific windows by ID or title, reducing image payload size and enabling focused automation on multi-window systems. Supports optional ROI cropping for further optimization.

vs others: More efficient than full-screen capture because it reduces image size and processing overhead, allowing agents to focus on relevant UI areas and reducing latency in multi-window environments.

via “targeted element screenshot extraction”

** - Capture website screenshots including full page, elements, and device specific sizes.

Unique: Provides selector-based element extraction through MCP, allowing LLM agents to request specific component screenshots by CSS selector without parsing page HTML or managing browser state directly

vs others: More precise than full-page screenshots for component testing and reduces image size/processing overhead by capturing only the target element region

via “full-screen and region screenshot capture”

** - Programmatic control over Windows system operations including mouse, keyboard, window management, and screen capture using nut.js.

Unique: Abstracts Windows GDI screenshot operations through nut.js, providing a simple synchronous API for full-screen and region captures without requiring developers to manage device contexts or bitmap handles directly

vs others: Faster than external screenshot tools because it's in-process; more flexible than built-in Windows screenshot because it supports region capture and programmatic integration

via “screenshot-capture-with-region-selection”