"Chrome DevTools - Browser Developer Tools & Debugging Resources" matching MCP servers:

Enables LLMs to automatically diagnose coding errors through codebase search, test execution, and live debugger integration (DAP/V8 CDP). Provides a secure, policy-gated environment for investigating failures while preventing destructive operations.

Lets an AI assistant inspect a running Google Chrome or Microsoft Edge browser over the Chrome DevTools Protocol, reading console output, network traffic, DOM snapshots, errors, performance metrics, storage, and screenshots. It also supports live event subscriptions, screen-frame streaming, and one-shot export of everything into an offline-readable archive such as HAR, CSV, and a self-contained HTML report.

Enables AI agents to attach to Chrome tabs, set breakpoints, inspect scope variables, evaluate expressions, and step through code using Chrome DevTools Protocol for runtime debugging.

Provides intelligent error detection and debugging capabilities across multiple programming languages with real-time monitoring of build, lint, runtime, console, and test errors. Offers AI-enhanced error analysis with automated resolution suggestions and context-aware debugging.

MCP server wrapping Backstage — query service catalog, fetch TechDocs, and scaffold services via AI agents.

Enables AI agents to inspect and control a live Chromium browser for frontend debugging, providing console logs, network requests, DOM snapshots, and accessibility analysis.

A production-grade MCP server for Windows developer environments, providing AI assistants with secure terminal, filesystem, git, Docker, SQLite, browser, and GitHub capabilities through 12 providers and 40+ tools.

Production-grade MCP server that gives AI agents safe access to your local dev environment: filesystem, databases, processes, and OpenAPI specs.

Search and resolve MCP server packages on the mpak registry, enabling agents to discover servers, inspect tools, and check credential requirements before installation.

Gives AI agents a compact, semantic interface to the browser, returning structured page snapshots with stable element IDs instead of raw DOM. Enables agents to navigate, interact, and extract information from web pages efficiently.

A Model Context Protocol (MCP) server for Chrome Enterprise Premium that exposes DLP rules, content detectors, connector policies, browser telemetry, and license management as MCP tools, enabling any MCP-compatible AI agent to inspect and configure a Chrome Enterprise environment.

Enables web search via a real browser for any LLM, bypassing anti-bot measures without API keys.

A Python framework that exposes developer tools through MCP, REST API, and CLI from a single shared registry, enabling AI assistants, HTTP clients, and terminal users to access the same tools.

Exposes a headful, collaborative browser over MCP so an agent and a user can share one visible Chromium window: the agent navigates, reads, clicks, types, and fills forms, while the user watches and can take over for passwords, CAPTCHAs, 2FA, and payments, with every action written to an append-only audit trail. It also runs headless under a chat-based harness.

Use your signed-in Chrome through a local MCP server and companion extension. Supports tabs, navigation, forms, screenshots, and page reading, with a deny-all domain allowlist. Install: npx -y mcp-browser-extension@latest.

Provides web automation utilities, including taking Camofox browser tab screenshots and saving them directly to local files.

parallel-browser-mcp is an MCP server for parallel browser automation. It exposes a numeric session model over MCP so one client can create and control multiple browser sessions at the same time across multiple browser providers.

A lightweight MCP server providing common utility tools such as unit conversion, password generation, date calculation, hashing, Base64 encoding/decoding, UUID generation, text case conversion, and text statistics, with no external dependencies.

Enables inspecting local OpenAPI specification files by listing and retrieving spec details with filters and $ref resolution.

Enables natural-language interaction with Ladybug Tools for building performance simulation, 3D modeling, and data visualization, allowing agents to perform complex environmental analysis tasks without CAD software.