"Creating custom experimental images in Docker" matching MCP servers:

Enables AI coding assistants to control a persistent Chrome browser through DevTools Protocol. Supports web navigation, screenshots, element interaction, mobile testing, console logs, and network debugging in a stable Docker environment.

Enables MCP clients to open websites, capture screenshots at multiple breakpoints, and run automated design audits for issues like overflow, small tap targets, low contrast, missing alt text, tiny text, and clipped content.

Enables AI agents to inspect UI screen recordings as token-efficient image sheets with changed regions boxed, single-frame flicker flagged, and speech aligned to frames. Exposes watch_video and zoom_video tools so agents can pull dense keyframes across any time range or one sharp full-resolution frame, all processed locally.

Enables AI agents and MCP clients to drive real Chrome, Edge, Brave, and Firefox browsers through native automation protocols. It exposes tools for navigation, page snapshots, form filling, clicks, screenshots, console/network inspection, and tab management.

Enables testing of secure MCP connections with custom CA certificates and verified TLS.

Exposes repository reconnaissance tools — file listing and verbatim reading, regex search, dependency-tree parsing across Python/Node/Rust/Go, and test-coverage ingestion — so agents can gather line-numbered evidence from real source files. This lets an audit pipeline physically re-verify every claim and drop findings that cannot be located in the code.

Lets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.

Enables AI-driven management of Zephyr test assets and Jira integrations through natural language, including creating test cases, steps, cycles, and linking tests to issues.

Provides prompt templates and tools for mobile QA automation, including converting manual tests to Appium scripts, analyzing failures, and standardizing bug reports.

MCP server for remote debugging of Apps in Toss mini-apps, providing CDP relay and test runner capabilities.

Provides tools for generating fake data using Faker.js, including person, lorem, internet, and more, with support for multiple locales and customizable options.

MCP server for the Ghost Inspector API that lets agents inspect and manage end-to-end browser tests, offering read-only tools for inventory, module usage, and stale test analysis, plus opt-in guarded write operations.

Renders HTML in a headless browser and detects layout defects numerically, such as text overlaps, placeholder leftovers, and element overflow, without requiring baseline images.

Provides instant GDScript syntax validation and diagnostics by bridging Godot's native Language Server Protocol to MCP clients. Enables real-time syntax checking in AI assistants without requiring custom plugins or context switching to the Godot editor.

Scan any project or website for privacy compliance issues directly in your AI coding tool. Detects tracking tech, cookies, and third-party data collection. Works in Claude Code, Cursor, and Windsurf.

MCP server for managing test cases, cycles, and results in Zephyr Scale on self-hosted Jira. Enables AI agents to create, search, update, and delete test artifacts through natural language.

Run the requests in a Postman collection folder and get structured, assertion-level results back straight from your AI assistant.

Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.

Enables AI coding agents to test and automate Android apps rendering UI in OpenGL/Vulkan/Metal surfaces via screenshot-based vision tools for tapping, swiping, long-pressing, and recording, without accessibility tree dependency.

Enables pixel-perfect image comparison and visual regression testing via tools to compare local images, screenshots, and URLs with detailed diff statistics.