"Integrations or tools that work with n8n automation software" matching MCP connectors:
GET /v1/connectors — MCP directory API referenceMatching Connector Tools:
Vouch — independently measured reliability scores for MCP tools, not self-reported claims.
Search + patterns, maturity assessment, context pricing, redaction checks - a practice as tools.
Stateful WebSocket session registry with per-connection Shannon entropy delta tracking for schema di
Dead-man switch monitors for cron & AI agents with dependency-cascade alerts. No account needed.
Discover Frontier inference capabilities and read sanitized usage through read-only tools.
CVE intelligence: exploitation (KEV/EPSS), detection coverage, fixed versions. All tools keyless.
A managed runtime for custom API integrations. Manage lines, endpoints, keys, logs and DLQ via MCP.
Monitoring for agencies — uptime, SSL, DNS, blocklists, AI visibility, MCP health. 8 no-auth tools.
Live status for 172 cloud and SaaS vendors from their official feeds. Is it you, or is it them?
186 real AI agent post-mortems, 107 of them measurement failures. Free tools, paid via x402.
EU AI Act Art-14 runtime oversight: allow / flag / gate-to-human on an agent action, with receipt.
Read-only triage for n8n, MCP, webhook, and AI agent workflow production failures.
Uptime, API and server monitoring with outages, reporting, on-call and status pages.
An inter-agent graffiti wall for one completely optional trace.
Read monitors, incidents, heartbeats, on-call and status pages; acknowledge or resolve incidents.
Live reliability for AI agent tools: is it working right now, and how do I call it correctly?
Diagnose AI workflows for failure, security, and handoff risks — RED/AMBER/GREEN per node.
Public MCP digital twin with synthetic systems and an agent firewall. No customer data.
AI-ready vendor incident status with public active incidents and plan-scoped history.
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.
