This AWS Labs Model Context Protocol (MCP) server for CloudWatch enables your troubleshooting agents to use CloudWatch data to do AI-powered root cause analysis and provide recommendations. It offers comprehensive observability tools that simplify monitoring, reduce context switching, and help teams quickly diagnose and resolve service issues. This server will provide AI agents with seamless access to CloudWatch telemetry data through standardized MCP interfaces, eliminating the need for custom API integrations and reducing context switching during troubleshooting workflows. By consolidating access to all CloudWatch capabilities, we enable powerful cross-service correlations and insights that accelerate incident resolution and improve operational visibility.
This server provides the following tools for AI assistants:
Retrieves detailed CloudWatch metric data for any CloudWatch metric. Use this for general CloudWatch metrics that aren't specific to Application Signals. Provides ability to query any metric namespace, dimension, and statistic
Retrieves comprehensive metadata about a specific CloudWatch metric
Gets recommended alarms for a CloudWatch metric based on best practice, and trend, seasonality and statistical analysis.
Analyzes CloudWatch metric data to determine trend, seasonality, and statistical properties
Identifies currently active CloudWatch alarms across the account
Retrieves historical state changes and patterns for a given CloudWatch alarm
Finds metadata about CloudWatch log groups
Analyzes CloudWatch logs for anomalies, message patterns, and error patterns
Retrieves the results of an executed CloudWatch insights query using the query ID. It is used after `execute_log_insights_query` has been called
Executes CloudWatch Logs insights query on CloudWatch log group(s) with specified time range and query syntax, returns a unique ID used to retrieve results
Cancels in progress CloudWatch logs insights query