AWS CloudWatch is a Monitoring MCP server that lets Claude Code, Cursor, Windsurf and any MCP-compatible AI agent query CloudWatch metrics, logs, and alarms to monitor AWS resources from AI agents. Install in 1 minute with mcpizy install cloudwatch.
Monitoring
Query CloudWatch metrics, logs, and alarms to monitor AWS resources from AI agents.
mcpizy install cloudwatchpip install mcp-server-cloudwatchAccess OpenTelemetry traces and metrics via Pydantic Logfire. Debug production issues fast.
Crash reporting and real user monitoring data. Track errors, performance, and user sessions.
Real-time production context — logs, metrics, traces. SRE intelligence for incident response.
Query metrics and alerts
If AWS CloudWatch doesn't fit your stack, these Monitoring MCP servers solve similar problems.
The AWS CloudWatch MCP server is an Monitoring Model Context Protocol server that lets Claude Code, Cursor, Windsurf, VS Code with Copilot, and other MCP-compatible AI agents query CloudWatch metrics, logs, and alarms to monitor AWS resources from AI agents. It exposes AWS CloudWatch's capabilities as tools the AI can call directly from your editor or CLI.
The fastest way is the MCPizy CLI: run `mcpizy install cloudwatch` and MCPizy will add the server to your `.claude.json` automatically. You can also install it manually by adding an entry under `mcpServers` in `.claude.json` with the command `pip install mcp-server-cloudwatch` and restarting Claude Code.
Yes. The AWS CloudWatch MCP server is free and open source (see the GitHub repository linked on this page). You may still need a AWS CloudWatch account or API key to connect the server to the underlying service, but the MCP layer itself has no MCPizy subscription cost.
Yes. Any MCP-compatible client works — including Claude Code, Claude Desktop, Cursor (via `.cursor/mcp.json`), Windsurf, VS Code with Copilot Chat, and custom agents built on the MCP SDK. The same install command targets all of them; only the config file path differs.
Once installed, your AI agent can query CloudWatch metrics, logs, and alarms to monitor AWS resources from AI agents directly inside your conversation. Typical use cases include asking Claude Code or Cursor to run AWS CloudWatch operations, inspect results, chain AWS CloudWatch with other MCP servers (see our Workflow Recipes), and automate repetitive monitoring tasks without leaving your editor.