Browser Tools Mcp

This project is a browser automation toolset and Model Context Protocol server that connects large language models to live browser sessions. It provides a web debugging interface and a quality auditor to facilitate the analysis of document object model structures and browser logs.

The system implements a bridge that streams diagnostics into AI-powered editors, allowing for the automated identification of web bugs. It features a data sanitization pipeline that removes cookies and sensitive headers to prevent private information leakage during the analysis process.

The toolset covers a range of capabilities including real-time log monitoring, page screenshot capture, and structural analysis of DOM elements. It also includes auditing tools for evaluating web performance, accessibility, and search engine optimization.

Features

Model Context Protocol Implementations - Implements the Model Context Protocol to expose browser utilities as tools for AI-enabled IDEs.

Agentic Web Interaction - Enables AI agents to navigate and interact with live web data directly within the browser.

MCP Protocol Integrations - Extends MCP-compatible editors with direct access to browser diagnostic data.

Model Context Protocol Servers - Implements an MCP server that connects LLMs to live browser sessions for structural and log analysis.

Document Element Inspection - Retrieves and modifies selected elements from the browser DOM for direct structural analysis.

Frontend Debugging Workflows - Links live browser DOM elements and page states to a code editor to accelerate UI iteration.

Agent Context Mappings - Maps browser focus to structural DOM representations to provide essential context for AI agents.

Data Sanitization - Strips cookies and sensitive headers from browser logs to prevent private information leakage to LLMs.

Browser Log Streaming - Captures and filters live browser console and network events to isolate runtime errors.

Log Analysis - Provides tools to parse and interpret browser console logs and network requests to identify application errors.

Diagnostic and Error Reporting - Retrieves console logs, errors, and network requests to identify runtime issues in web applications.

Real-Time Application Log Monitoring - Streams browser console logs and network events in real time for immediate debugging within an editor.

Page State Captures - Captures the browser page as screenshots and HTML dumps to connect the UI with the underlying codebase.

AI Context Mapping - Tracks and captures page element data to provide essential structural context for AI agents.

Browser Automation - Provides programmatic control of web browsers for DOM extraction and automation.

Web Application Debugging - Analyzes browser console logs and network requests within an IDE to fix runtime errors.

Browser Automation Agents - Uses AI agents to verify visual and structural correctness through automated browser interaction.

AI-Driven Debugging Workflows - Combines tools and AI prompts into automated workflows for identifying and resolving web bugs.

Screenshot Capture - Captures visual snapshots of the browser state for documentation and state verification.

Visual State Analysis - Integrates browser screenshots into an editor for LLM-driven visual analysis of the UI state.

Continuous Web Auditing Tools - Provides automated tools for evaluating accessibility, performance, and SEO metrics based on development best practices.

Web Quality Auditors - Evaluates accessibility, performance, and SEO metrics using industry development standards.

Performance Auditing - Runs performance and code quality scans using industry-standard tools to optimize web application delivery.

AgentDeskAIbrowser-tools-mcp

Features

Star history