"How to Run a Python Script" matching MCP connectors:
Matching Connector Tools:
290+ quality-scored API capabilities for AI agents across 27 countries via MCP.
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/
Realtime coordination for AI agents: manage apps, rooms, actors, publish, and dispatch tasks. Visit https://nolag.app and setup your account.
Monitor uptime and incidents, run checks, and publish status updates from your Uptimepage org.
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.
Measured latency, time to first token and uptime for ~45 AI inference APIs, by region.
Free MCP window into a live autonomous machine-economy experiment: telemetry, hypothesis scoreboard.
Free anonymous website, DNS, email and TLS checks, plus read-only access to your monitors.
performance-review MCP — wraps StupidAPIs (requires X-API-Key)
Zero-trust gateway for AI agents: score tool calls, verify agent cards, enforce policy, audit.
Cloudflare Radar MCP — internet observatory (traffic, attacks, BGP, quality)
Live status and health checks for AI coding providers: Claude, Cursor, Copilot, Codex and more.
Live reliability for AI agent tools: is it working right now, and how do I call it correctly?
Enterprise AI Control Plane: governance, guardrails, spend tracking, compliance & smart routing.
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.
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.