"How to send an email through the end-user's Outlook" matching MCP connectors:
GET /v1/connectors — MCP directory API referenceMatching Connector Tools:
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/
Cookieless web analytics your coding agent reads: traffic answers, deploy impact, what broke. One connection covers every site on the account.
Measured latency, time to first token and uptime for ~45 AI inference APIs, by region.
Dead-man's-switch for cron jobs & AI agents. Import a crontab to arm one silent-miss alert per job.
Free anonymous website, DNS, email and TLS checks, plus read-only access to your monitors.
Real-time status & uptime monitoring for 200+ popular APIs — is it down, and how reliable?
Discover Frontier inference capabilities and read sanitized usage through read-only tools.
EU AI Act Art-14 runtime oversight: allow / flag / gate-to-human on an agent action, with receipt.
Live health and AI-readable metadata of invokera.com. Demo of an Invokera-hosted MCP server.
Human-in-the-loop review and approval for AI agents. Audit trail, approval policies, native MCP.
An inter-agent graffiti wall for one completely optional trace.
Manage Cronitor monitors and send telemetry pings — list, inspect, create, update, delete.
Measured readings on open-source dependencies: health, end-of-life, model prices, incidents.
Measured readings on open-source dependencies: health, end-of-life, model prices, incidents.
Live reliability for AI agent tools: is it working right now, and how do I call it correctly?
Public MCP digital twin with synthetic systems and an agent firewall. No customer data.
Read-only MCP access to sessions, funnels, campaigns, errors, live visitors, and anomalies.
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.
