"Cargo Doc" matching MCP servers:

Enables QA to interactively inspect DOM elements in a visible browser, generate resilient Robot Framework locators and Page Objects, and validate selectors via natural language.

DocGuard, an official, zero-dependency Node.js MCP server (npm: docguard-cli, docguard mcp) exposing 5 read-only doc-governance tools: guard, score, explain, verify-claims, diagnose.

Drives a real Chromium browser to extract a page's DOM into compact Semantic JSON with Playwright-native locators, enabling AI agents to generate consistent, accurate Playwright tests.

Enables AI agents to record and time-travel through DOM mutations, inspect and interact with live pages, capture screenshots, and diagnose UI disappearance or layout changes through a 34-tool MCP interface.

Enables automated graphics capture and replay with RenderDoc, allowing agents to launch executables, inspect captures, and export thumbnails or timeline JSON for regression triage.

Security scanner for MCP servers and AI-generated code. Detects leaked API keys, PII, prompt injection, and MCP misconfigs with A-F security grades.

Enables LLMs to manage test cases, cycles, and executions in Jira Data Center via Zephyr Scale.

Unity Editor adapter for the DCC MCP ecosystem, providing tools to inspect and modify Unity projects, scenes, builds, and diagnostics through a WebSocket bridge.

A Puppeteer-based server designed for remote debugging, E2E testing, and DOM inspection of frontend applications. It allows users to interact with page elements, capture screenshots, monitor network traffic, and execute JavaScript directly within a browser environment.

Browser-backed QA MCP server that lets coding agents test web apps, collect browser evidence, and return fix-ready reports before shipping.

Enables MCP-driven testing and verification of Facebook and TikTok connections, including OAuth flows and connection status checks.

Exposes Pi execution nodes to any MCP client so it can dispatch sub-tasks to local or cloud models, poll and cancel them, and get command-based acceptance, diff-based evidence chains and automatic escalation to stronger models on failure.