"How to execute tests on a frontend page and debug browser console issues" matching MCP servers:

Enables AI tools to interact with browsers for enhanced frontend development, providing context to LLMs through tools like API call analysis, screenshots, element selection, and documentation ingestion.

A zero-dependency local MCP server that audits and auto-fixes React/Next.js frontend code smells, unused imports, and responsive UI issues

MCP server for declarative intent execution. Agents submit intents — transfers, swaps, multi-hop routes — and the engine resolves and executes them atomically.

A minimal MCP server with get_weather and create_ticket tools, used for testing MCP servers across protocol, unit, eval, transport, and auth layers.

Enables frontend navigation and browser automation through a deterministic MCP server, integrating Browser Use and optional code graph hints for coding agents.

Detects tests that pass no matter what the code does, revealing vacuous tests that provide false confidence in coverage.

An intelligent debugging assistant that automates the debugging process by analyzing bugs, injecting HTTP-based debug logs into code across multiple environments (browser, Node.js, mobile, etc.), and iteratively fixing issues based on real-time feedback.

Enables coding agents to visually verify front-end changes by capturing screenshots at real breakpoints, identifying the single element responsible for overflow, and surfacing console and network errors.

Enables triage of GitHub issues by fetching and filtering issues, checking claim status, and listing new open issues across a watch-list.

Enables LLMs to list, create, and comment on GitHub issues using your own GitHub identity via stdio transport.

Gives AI coding agents a ten-phase frontend design pipeline (audit, style direction, build, review, verify, perf, brand) with an exclusive style menu. Its verify_page tool loads a URL in a real Playwright browser at mobile and desktop viewports and returns screenshots, ARIA snapshots, console errors, failed requests, horizontal-overflow offenders, axe-core violations, and pointer type.

An AI recipe recommendation server based on the MCP protocol, providing functions such as recipe query, classification filtering, intelligent dietary planning, and daily menu recommendation.

A server that enables retrieval of Sentry issues through two simple tools - get a specific issue by URL/ID or get a list of issues from a project.

Enables an AI agent to read and operate the Chrome or Edge tabs you are already using — titles, URLs, text, HTML, screenshots, clicks, typing, scrolling, and tab management — via a browser extension and native messaging, without restarting the browser, switching profiles, or opening a debug port. It exposes these capabilities as 19 MCP tools, with access limited to explicitly shared tabs and hardened by per-tab, origin-bound grants plus domain allow/deny lists.

An MCP server that adds a safety layer with eight independent checks and audit logging between a language model and a broker order, supporting both on-disk simulation and live MetaTrader 5 terminals.

Drop a single script into a web UI and the page itself exposes DOM inspection, interaction, and diagnostic devtools to coding agents through document.modelContext, with no server, LLM, or chat UI involved. It also lets an app register its own domain-state tools, so agents can reach state that the DOM cannot reveal.

Enables coding agents to load frontend anti-slop rules and deterministically verify generated React, Next.js, and Tailwind UI before completion. It audits for common AI-generated patterns, checks stylesheet/token wiring, provides real or placeholder images, and verifies TypeScript compilation.

MCP server for InterSystems IRIS that provides 8 tools including ObjectScript execution, class compilation, and unit testing with a fast DirectTestRunner.

A standalone MCP server that exposes a narrow set of GitHub Issues tools for a single repository, securely handling a GitHub personal access token so agents never see the credential.

Enables AI assistants to list, search, and inspect issues on any public GitHub repository via natural language.

Starter scaffold for a workshop to build an MCP API testing server, intended to be extended through incremental steps.

Exposes GitHub issue operations as tools for LLM agents, enabling listing, creating, commenting on, closing, and searching issues via natural language.

Lets agents read a GitHub repository's live issues through read-only tools for listing, filtering, fetching details with comments and cross-references, searching, and exploring labels and milestones, with transparent pagination and provenance notes.

Provides AI assistants with frontend development tools including component scaffolding, bundle analysis, accessibility checks, and responsive design guidance. Enables automated generation of React components with tests and stories, bundle optimization recommendations, and WCAG compliance fixes.

A comprehensive MCP server for frontend analysis. It provides Google Lighthouse audits, code quality checks, SEO metadata verification, accessibility analysis, and bundle optimization for React and Next.js projects.

Enables browser-based web page fetching with automatic cookie management and CSS selector extraction, supporting JavaScript rendering and authenticated sessions through a Chrome extension.

Enables AI assistants to perform interactive Python debugging with breakpoints, step execution, and variable inspection using the Debug Adapter Protocol (DAP) through an MCP server interface.

MCP server for managing Apidog test cases, scenarios, suites, and test data, providing full read/write access to Apidog's test management features.

Enables AI agents to manage GitHub issues by listing, creating, searching, and commenting on them through MCP.

An MCP server implementing a 7-stage agentic frontend workflow—from design audit to PR review—including AI-driven component generation, browser validation, E2E testing, and CI self-healing.