Ai Ops MCP server. Tools: system health check, check service, security scan. Built by MEOK A...
Ai Ops MCP server. Tools: system health check, check service, security scan. Built by MEOK A... Exposed over MCP by the ai mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
This MCP server is built with EU AI Act compliance built-in:
Everything the assistant can do here goes through one of these:
Well — documented APIProduction-ready — Active maintenancePro — ** $99/mo — Full MCP suite + EU AI Act trackingEnterprise — ** $499/mo — Custom dev + SLA + Dedicated supportDomain — PurposeTier — Price@smithery/cli on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Plenty of monitoring and observability servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Ai's toolset — Well, Production-ready, Pro and 3 more — is a fair guide to whether it matches your workflow. It is maintained by CSOAI-ORG; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Well | documented API |
| Production-ready | Active maintenance |
| Pro | ** $99/mo — Full MCP suite + EU AI Act tracking |
| Enterprise | ** $499/mo — Custom dev + SLA + Dedicated support |
| Domain | Purpose |
| Tier | Price |
{
"mcpServers": {
"ai-ops-mcp": {
"command": "uvx",
"args": ["ai-ops-mcp"]
}
}
}Configuration as documented by the project. Restart the client after saving.
Give your coding agent the full DevTools toolbox: traces, network, console, heap snapshots and Lighthouse.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.
Errors with full context — stack traces, issue triage and AI-powered root-cause analysis from Sentry's server.
Enables enhanced web research capabilities for large language models through intelligent search queuing and advanced content extraction.
Automates browser interactions and enables Large Language Models (LLMs) to interact with web pages through Playwright and Chrome DevTools Protocol
Guides tool usage by providing recommendations for MCP tools at each problem-solving stage.