"Overview of Command Line Interface Usage" matching MCP connectors:

Core Web Vitals metrics by CMS, CDN, and framework — free remote MCP, no auth.

Track cost, latency, and usage of every MCP tool call from any client (Claude, Cursor, Windsurf). Free 25K calls/month — open-source proxy, EU-hosted.

Gain visibility into the performance, availability, and health of your apps and infrastructure.

The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costs—all without needing to parse text output or use complex kubectl commands.

Connect AI assistants to AppAmbit — the command center for your mobile & desktop apps. Query real-time analytics, sessions, and crash reports; read and push remote config; send push notifications, provision and query managed per-app SQLite databases, deploy serverless Cloud Code functions; and manage a headless CMS. Also generates SDK setup snippets and runs integration diagnostics. Supports .NET MAUI, Swift, Objective-C, Android and more. Built for indie devs, mobile teams, and agencies.

Analytics and debugging for your MCP server — explore usage and sessions, then root-cause errors.

A paid remote MCP for Decapod, built to return verdicts, receipts, usage logs, and audit-ready JSON.

A paid remote MCP for Skybridge, built to return verdicts, receipts, usage logs, and audit-ready JSO

A paid remote MCP for AI SDK MCP gateway registry, built to return verdicts, receipts, usage logs, a

A paid remote MCP for AI SDK data query MCP, built to return verdicts, receipts, usage logs, and aud

The Polar Signals MCP server enables AI assistants to connect directly with performance profiling data, allowing users to analyze application performance through natural language queries. Key capabilities include querying CPU performance and memory usage, exploring profiling metadata like profile types and labels, and providing AI-driven code optimization suggestions directly within development environments like Claude Code or Cursor.