"An explanation or exploration of reasoning" matching MCP connectors:
Matching Connector Tools:
Core Web Vitals metrics by CMS, CDN, and framework — free remote MCP, no auth.
Read monitors, incidents, heartbeats, on-call and status pages; acknowledge or resolve incidents.
Public MCP digital twin with synthetic systems and an agent firewall. No customer data.
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.
An MCP server giving access to Grafana dashboards, data and more.
High-performance array aggregation and metrics clearing engine. Cleans and bucket-groups noisy metric streams via an $O(N)$ single-pass data sweep. Operates natively with the pay-per-call x402 micropayment framework.
Uptime, SSL, DNS and domain monitoring you can talk to from Claude or any MCP client.
Mobile observability for AI agents. Investigate crashes, hangs, ANRs, bugs, and app performance, and triage app store reviews, directly from your IDE or terminal.
Log, evaluate, and ground AI decisions against authority context. Returns PASS, WARN, or BLOCK.
MCP-native AI SRE. Exposes your production OpenTelemetry problems, traces, and logs over the Model Context Protocol, plus an AI remediation loop that opens a reviewed GitHub fix PR and verifies in production (reopening on regression). Tools include list_problems, get_problem, query_traces, detect_anomalies, and request_problem_remediation. Human-in-the-loop by default — the merge button stays yours.
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.