Enhanced monitoring with CloudWatch and Application Signals MCP

Learn how CloudWatch and Application Signals MCP servers let developers query service health, errors, logs, and SLOs in natural language from the CLI — eliminating console-hopping and drastically reducing the Mean Time to Resolution (MTTR).

11 mins read

Oct 24, 2025

It's not uncommon for developers to waste time jumping between different monitoring screens. When something breaks, they jump from CloudWatch to X-Ray, from basic logs to alarms, eventually losing focus and slowing down their work. This scattered data makes figuring out what’s wrong difficult and increases the dreaded Mean Time To Resolution (MTTR).

The solution isn’t more dashboards, it’s intelligence in your terminal.

The Model Context Protocol (MCP) servers for CloudWatch and Application Signals are powerful local agents that link rich, detailed operational data directly into your developer tools (like your IDE or CLI) using simple, normal questions.

This newsletter walks you through the Model Context Protocol (MCP) servers, the AI bridge directly connecting CloudWatch and Application Signals data to your CLI. We’ll specifically discuss:

What CloudWatch and Application Signals MCP servers are and how they differ.
The core mechanism behind natural language queries transforming into deep AWS insights.
How to deploy a sample application using Amazon Q CLI.
A hands-on demonstration of rapid incident triage using Amazon Q CLI and MCP servers.

Understanding the MCP servers#

The MCP is an open protocol that standardizes how AI applications access external tools. AWS uses specialized MCP servers to interface with its observability services:

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Written By:

Fahim ul Haq

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MCP Server	Primary Focus	Primary Use
CloudWatch MCP Server	Resource-level metrics, logs, and alarms (e.g., CPU, memory, log streams)	Analyzing raw telemetry, complex log patterns (via Logs Insights), and general infrastructure health.
Application Signals MCP Server	Service-level health, SLO compliance, and distributed tracing (via X-Ray)	Understanding application performance, identifying bottlenecks, and root cause analysis against business goals.