"Transferring a Dockerized Website from Local to Remote Server Using Cursor AI" matching MCP servers:

Enables auditing any public website, returning a scored plain-English report that flags issues costing customers, covering speed, phone experience, search visibility, contact options, writing quality, and modernity.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Provides a testable gateway path for running a local MCP-style server in hosted-like conditions, with tenant-isolated reads, approval-denied writes, deterministic failures, and smoke-test verification.

Provides accessibility testing capabilities through CLI, helping identify accessibility issues in web applications using axe-core and Puppeteer.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

A local MCP server that gives Cursor Agent three focused QA tools for analyzing user stories, creating test charters, and drafting bug reports, with built-in safety rules and reusable workflows.

MCP server that brings Trustabl static reliability scanning into Cursor, letting agents inventory agents/tools, flag risky patterns, score production readiness, and write SARIF reports.

Extracts and analyzes key frames from Loom videos or local video files for debugging purposes. Automatically detects scene changes and provides visual context for troubleshooting issues.

A local MCP server that connects Claude Desktop, Claude Code, and Claude Chat to your Azure DevOps organization, enabling querying work items, reviewing pull requests, triggering pipelines, searching code, managing test plans, and updating wikis using plain language.

A licensed, locally run Playwright E2E testing MCP server for Claude Desktop and Cursor. It lets AI coding agents inspect semantic accessibility structure, validate role-based browser flows with explicit assertions, and generate reusable Playwright tests.

Enables turning requirements documents into RFCs, functional test scenarios, and a traceability matrix, with tools to parse, diff, and verify coverage.

Enables LLM coding agents to inspect, control, and verify remote ROS2 robotics applications without a local ROS2 installation, supporting topics, services, actions, parameters, TF transforms, and automated test suites.

Automates the conversion of Figma designs into TypeScript React components and integrates with GitHub to create pull requests for the generated code. It includes visual regression testing with Playwright and accessibility validation to ensure implementations match the original designs.

A production-minded MCP server that enables autonomous website design and management, with resources, safe Git operations, validation, Playwright inspection, Vercel previews, and JSONL audit logging. It also provides specialized audit agents for design, SEO, performance, accessibility, conversion, diagnostics, and landing pages.

Enables learning and integration testing of the Model Context Protocol by evolving from a minimal server toward production-grade tools, resources, prompts, and agent interoperability.

A multi-language testing automation server that enables AI assistants to discover, plan, and execute tests across six major programming languages. It provides comprehensive tools for repository scanning, automated test execution, and detailed coverage reporting.

Guarded, profile-driven Charles Proxy automation for macOS and Android testing. Uses immutable TOML profiles and explicit plan/apply confirmation.

A local MCP server that lets Claude Code and Codex delegate repository exploration and test proposals to a remote LM Studio model, while enforcing security boundaries by keeping all repository access read-only and never applying patches or running commands remotely.

Spring AI Playground is a self-hosted web UI platform for building low-code tools and dynamically exposing them via built-in MCP server for AI agents.

An MCP server that automates code reviews through linting, testing, and git diff analysis. It also generates conventional commit messages and detailed pull request descriptions based on file changes and code patterns.