"Managing Kubernetes Clusters on AWS Elastic Beanstalk" matching MCP connectors:
GET /v1/connectors — MCP directory API referenceMatching Connector Tools:
Cookieless web analytics your coding agent reads: traffic answers, deploy impact, what broke. One connection covers every site on the account.
EU AI Act Art-14 runtime oversight: allow / flag / gate-to-human on an agent action, with receipt.
AI/LLM agent output audit MCP: policy eval, tamper-evident chain, AI safety, x402 USDC on Base.
Manage cron/heartbeat checks, read pings and flips, pause/resume/delete on Healthchecks.io.
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 monitors, incidents, heartbeats, on-call and status pages; acknowledge or resolve incidents.
Measured readings on open-source dependencies: health, end-of-life, model prices, incidents.
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.
Measured readings on open-source dependencies: health, end-of-life, model prices, incidents.
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.
Run a prompt through a LangChain (system + human) chain over Gemini on Vertex AI; optional LangSmith
Read-only cloud cost and infrastructure governance across AWS, Azure and GCP. 85 tools covering cost overview and trends, cost by provider/resource/tag/team, budgets, resources, schedules, recommendations, tagging policies, audit logs, anomalies, Kubernetes resources and pod logs. Hosted remote server, nothing to install. Docs: https://zop.dev/learn/mcp-server?utm_source=glama&utm_medium=listing&utm_campaign=mcp-directory Claude setup: https://zop.dev/learn/how-to/set-up-zopnight-mcp-for-claude
Official Spike MCP server for incident management, alerting, and on-call.
Build and monitor LLM apps on Orq.ai: AI gateway, agents, prompts, evals, traces.
MCP control plane for AI developers managing customer mail, DNS, projects, keys, and diagnostics.
Manage Kubernetes clusters, deployments, databases, secrets and observability on Mengi Cloud.
Draw your app's architecture on a live canvas and flag the bottlenecks and security gaps.
MCP-native AI SRE. Exposes your production OpenTelemetry problems, traces, and logs over the Model Context Protocol, plus an AI remediation loop that opens a reviewed GitHub fix PR and verifies in production (reopening on regression). Tools include list_problems, get_problem, query_traces, detect_anomalies, and request_problem_remediation. Human-in-the-loop by default — the merge button stays yours.
