"Windows Performance Toolkit and xperf.exe" matching MCP servers:

Read-only MCP server for diagnosing Windows crashes, stability, and gaming performance by reading event logs, crash dumps, hardware inventory, performance counters, and registry settings.

Analyzes websites like a senior performance engineer and produces prioritized Core Web Vitals recommendations. Provides 16 tools for detailed performance analysis, including Lighthouse, CrUX, WordPress and Cloudflare inspections.

Enables web performance analysis using Google PageSpeed Insights and Chrome UX Report APIs, providing compact metrics and diagnostics without local browser automation.

Exposes Windows system information and control tools including hardware stats, network details, and process monitoring. It enables AI applications to retrieve real-time data about CPU, memory, drives, and active system processes.

Enables comprehensive analysis of AWS Lambda performance including cold start tracking, memory utilization monitoring, cost analysis, and optimization recommendations. Provides real-time metrics, comparative analysis across multiple functions, and actionable insights to improve Lambda performance and reduce costs.

A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.

Connect AI agents like Claude, Cursor, and Gemini to historical Windows performance and privacy data gathered by the AppControl Windows app. This server enables natural language queries into CPU, GPU, and temperature history alongside system security events for deep local auditing.

Enables read-only observability of Windows systems, exposing CPU, memory, storage, service, and health information through Model Context Protocol tools.

Enables AI agents to access real-time Windows PC context including active window, system performance, screen time, productivity analytics, and historical usage through MCP tools.

A Model Context Protocol server that provides LLM Agents with a comprehensive toolset for IP geolocation, network diagnostics, system monitoring, cryptographic operations, and QR code generation.

Maintenance, recovery, and observability for Claude Code. CLI + MCP server + dashboard.

A read-only MCP server for Linux system monitoring and OS algorithm simulation, exposing tools for memory, filesystem, process, and CPU scheduling observations.

Provides Windows system diagnostic capabilities to AI agents, allowing them to access event logs, crash information, system uptime, and perform stability analysis.

A secure-by-default MCP server and CLI for AI agents to inspect Spring Boot repositories and interact with runtime Actuator endpoints, enabling code review, dependency scanning, and monitoring.

Enables LLM agents to perform SRE reliability calculations like error budgets and burn rates using deterministic tools, integrating with Prometheus and Loki for real data.

Collects page performance metrics using Playwright, CDP, and Lighthouse, and returns a normalized PerformanceReport.

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.

MCP server providing read-only operational tools (logs, metrics, traces, service health, config) for troubleshooting an environment, with one exception for toggling chaos scenarios.

Monitors and analyzes mobile application performance data to detect severe issues such as excessive memory usage and view count growth. Provides intelligent analysis with customizable rules and integrates seamlessly with development workflows.

Provides read-only visibility into Snowflake warehouse cost and query performance, including credit usage by warehouse, expensive queries, and heuristic right-sizing recommendations.