"Using Python to Locate and Retrieve Browser Element Selectors" matching MCP servers:

Enables AI agents to monitor and debug browser runtime errors, console logs, and page diagnostics in real time via a Chrome extension and local MCP server.

Enables AI assistants to connect to browser DevTools and backend debuggers for full-stack debugging, including frontend console, network, performance, and backend log analysis.

An MCP server that provides AI coding assistants with comprehensive browser automation and debugging capabilities using Playwright, including visual inspection, DOM debugging, and execution monitoring.

A Model Context Protocol (MCP) server for comprehensive monitoring and observability of systems using Langfuse.

A Docker-based workspace providing headless Chrome browser with CDP proxy and noVNC interface for browser automation and monitoring.

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

Enables natural language queries for device support, including resolving device references, retrieving device details, historical metrics, and checking provisioning status. Maintains conversation context for multi-turn interactions.

Captures browser console messages (log, warn, error, etc.) and makes them available to AI language models like Claude Code and Gemini CLI for seamless debugging and development.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

Enables real-time system monitoring and automation through MCP protocol with SSE transport, integrating with n8n workflows to check system health, query logs, and retrieve metrics from ABC system APIs. Supports natural language queries in Vietnamese and English for seamless system administration.

Enables analysis of AI agent traces to detect and fix failures using heuristic detectors, with no LLM calls required.

Enables automated review and analysis of network security controls (AWS Security Groups, Network ACLs) using natural language queries. Supports NSC configuration reviews, network segmentation testing, and identifying security gaps through conversational LLM interaction.

A comprehensive MCP server providing advanced Redmine project analytics, web automation via Playwright, and iTunes music integration. It enables detailed tracking of sprint metrics, bug counts, and team performance through a token-optimized tool architecture.

Enables AI agents and users to manage workspace files, monitor system metrics, take persistent notes, and retrieve weather data via MCP tools and resources.

MCP server for profiling Python processes using py-spy, supporting flamegraphs, stack dumps, and performance comparisons.

Enables mapping JavaScript error stack traces back to original source code, extracting context information to help developers locate and fix issues.

MCP Server for notify to telegram / weixin

Bridges Chrome DevTools with Cursor AI to enable debugging and monitoring, including console logs, network requests, screenshots, and element inspection.

A Python utility for adding usage tracking, analytics, and audit trails to MCP servers using SQLite-backed persistence. It enables developers to monitor tool, prompt, and resource activity and expose these statistics directly to LLM clients.

A powerful, lightweight Model Context Protocol (MCP) server built in Python that enables Artificial Intelligence assistants (Claude Desktop, Cursor, Antigravity, Claude Code, etc.) to manage and monitor your Proxmox VE cluster using natural language.