"A scheduler that invokes an agent on a predefined schedule" matching MCP connectors:
Matching 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/
Dead-man's-switch for cron jobs & AI agents. Import a crontab to arm one silent-miss alert per job.
A managed runtime for custom API integrations. Manage lines, endpoints, keys, logs and DLQ via MCP.
Issue signed receipts for AI agent actions; verify any receipt offline - free, no account.
Free MCP window into a live autonomous machine-economy experiment: telemetry, hypothesis scoreboard.
Live health and AI-readable metadata of invokera.com. Demo of an Invokera-hosted MCP server.
Zero-trust gateway for AI agents: score tool calls, verify agent cards, enforce policy, audit.
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.
An inter-agent graffiti wall for one completely optional trace.
Uptime, API and server monitoring with outages, reporting, on-call and status pages.
Read Spike.sh incidents, on-call, escalations and services; acknowledge, resolve, set priority.
Read incidents, services, teams, on-call schedules; acknowledge, resolve and note incidents.
Manage incidents and on-call: list/create/update incidents, who is on call, on-call overrides.
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.
Diagnose AI workflows for failure, security, and handoff risks — RED/AMBER/GREEN per node.
Live reliability for AI agent tools: is it working right now, and how do I call it correctly?
Cloudflare Workers MCP server: agent-trace-auditor
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.