"Interacting with AI models like OpenAI or Google models" matching MCP connectors:

290+ quality-scored API capabilities for AI agents across 27 countries via MCP.

AI compute infrastructure intelligence: facilities, supply chains, sovereign AI, export controls.

Real-time status for 75+ AI services (OpenAI, Anthropic, Cursor). No auth, CORS-enabled.

Live status and health checks for AI coding providers: Claude, Cursor, Copilot, Codex and more.

Measured latency & uptime for AI inference APIs, by region. Exposes a get_ai_api_latency tool.

Public MCP digital twin with synthetic systems and an agent firewall. No customer data.

Read monitors, incidents, heartbeats, on-call and status pages; acknowledge or resolve incidents.

Read AI-gateway analytics, configs, virtual keys, workspaces and users; log request feedback.

Read-only Increase banking observability plus one safe non-money-moving write, for AI agents.

Uptime and website monitoring for AI agents. Query monitor status, incidents, heartbeats, domain expiry, and status pages in your Vantaj Uptime workspace.

Diagnose AI workflows for failure, security, and handoff risks — RED/AMBER/GREEN per node.

Enterprise AI Control Plane: governance, guardrails, spend tracking, compliance & smart routing.

Proxy Gemini (Vertex AI) completions wrapped in OpenTelemetry trace spans; returns the answer plus t

Live reliability for AI agent tools: is it working right now, and how do I call it correctly?

Run a prompt through a LangChain (system + human) chain over Gemini on Vertex AI; optional LangSmith

Security gateway for AI agents: score tool calls, verify agent cards, enforce policy, audit.

Live status, API pricing and rate limits for ChatGPT, Claude, Gemini, Cursor and 42+ AI tools.

AI-ready vendor incident status with public active incidents and plan-scoped history.

Connect engineering metrics, DORA performance, and deploy risk scoring to any AI assistant. Score PRs for deployment risk using a 36-signal model, query team health, incidents, coverage, and more.

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.