"How to View console.log Output in Chrome" matching MCP connectors:
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Uptime monitoring, status pages and on-call in one place. Create and tune monitors for HTTP, TCP, DNS, ping, TLS certificates, domain expiry, heartbeats and scripted browser logins. Run a check on demand, read uptime and response-time history, acknowledge and resolve incidents, post incident updates, schedule maintenance windows and edit public status pages. Read-only until you grant write access.
Read your team's hosted journal from an AI agent: every machine's streams, in hub order.
Fixter's MCP provides a stream-lined agentic way to onboard, setup and use the Fixter monitoring and observability platform. Check out more at https://fixter.dev/
Free anonymous website, DNS, email and TLS checks, plus monitor read and opt-in write access.
Measured latency, time to first token and uptime for ~45 AI inference APIs, by region.
AI/LLM agent output audit MCP: policy eval, tamper-evident chain, AI safety, x402 USDC on Base.
Agent-native service discovery and purchase-intent routing to Stripe-hosted checkout.
Trace events to function runs to the failing step, and bulk-cancel runs.
Real-time status & uptime monitoring for 294 popular APIs — is it down, and how reliable?
Uptime and website monitoring for AI agents. Query monitor status, incidents, heartbeats, domain expiry, and status pages in your Vantaj Uptime workspace.
Know when cron jobs and AI agents stop running: create monitors and check in from your agent.
Read-only Yandex Metrika MCP. Query visits, sources, geo, devices and more in plain language.
Report-To group count, body discarded
Live reliability for AI agent tools: is it working right now, and how do I call it correctly?
Human-in-the-loop review and approval for AI agents. Audit trail, approval policies, native MCP.
Read-only MCP access to sessions, funnels, campaigns, errors, live visitors, and anomalies.
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.
Cloudflare Workers MCP server: llm-output-quality-monitor
The Cortex MCP server provides read-only access to real-time engineering context from the Cortex developer portal, allowing AI coding assistants to answer natural language questions about your organization's catalog (microservices, libraries, domains, teams, infrastructure), scorecards (engineering standards and best practices), initiatives (goals and deadlines), and Engineering Intelligence metrics. It includes tools for querying documentation, tracking personal entities, and accessing AI-assisted insights across the entire Cortex ecosystem.
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.
