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Amazon CloudWatch and Application Signals MCP servers for AI-assisted troubleshooting

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Today, AWS announces two new Model Context Protocol (MCP) servers in the AWS Labs MCP open-source repository : CloudWatch MCP server and Application Signals MCP server. These servers enable AI agents to leverage comprehensive observability capabilities for automated troubleshooting and monitoring. The MCP servers allow AI assistants to analyze metrics, alarms, logs, traces, and service health data across your AWS environment to quickly identify and diagnose issues through simple conversational interfaces.

The MCP servers provide curated sets of tools designed specifically for operational troubleshooting scenarios. The CloudWatch MCP server supports alarm-based incident response, metric analysis, and log pattern detection, while the Application Signals MCP server enables service health monitoring through Service Level Objectives (SLOs), and automated root cause analysis using OpenTelemetry data. By leveraging the MCP standard, AI agents can perform complex troubleshooting workflows through natural language interactions, from analyzing alarm patterns, and metric anomalies to investigating service health issues and querying logs and traces. Rather than requiring developers to manually navigate multiple AWS consoles and APIs, these MCP servers enable AI agents to orchestrate these interactions intelligently while reducing the development times typically required for API integrations.

The CloudWatch MCP server can be used with CloudWatch in all AWS regions, and the Application Signals MCP server can be used in all regions where Application Signals is available.

To download and try out these open-source MCP servers locally with your AI-enabled IDE of choice, visit the AWS Labs MCP open-source repository .

AWS Parallel Computing Service (PCS) is now available in the AWS Europe (London) Region

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Today, AWS launches AWS Parallel Computing Service (PCS) in the AWS Europe (London) region, enabling you to easily build and manage High Performance Computing (HPC) clusters using the Slurm workload manager.

AWS PCS is a managed service that makes it easier for you to run and scale your high performance computing (HPC) workloads and build scientific and engineering models on AWS using Slurm. You can use AWS PCS to build complete, elastic environments that integrate compute, storage, networking, and visualization tools. AWS PCS simplifies cluster operations with managed updates and built-in observability features, helping to remove the burden of maintenance. You can work in a familiar environment, focusing on your research and innovation instead of worrying about infrastructure.

To get started, visit the AWS PCS page and the AWS PCS documentation .

Amazon VPC Lattice announces support for Oracle Database@AWS

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With VPC Lattice support for Oracle Database@AWS (ODB) you can now connect your applications in VPCs and on-premises to your ODB network. You can also leverage VPC Lattice to privately and securely access Amazon S3 and Amazon Redshift from your Oracle Exadata workloads.

With this launch, your ODB databases can easily connect to AWS services, HTTP APIs and TCP applications, across thousands of VPCs and on-premises, without the need to setup complex networking. VPC Lattice simplifies network management and provides centralized visibility. You can also use ODB managed integrations (powered by VPC Lattice) to privately and securely access Amazon S3 and Amazon Redshift. With a few clicks, you can enable OCI managed backup of your ODB databases to Amazon S3, or configure your own Amazon S3 backup. Additionally, the Zero-ETL integration connects ODB databases to Amazon Redshift to analyze data across multiple databases.

VPC Lattice support is available in all AWS Regions where Oracle Database@AWS is generally available.

To get started, use the AWS Management Console to provision Oracle Database@AWS resources. You can use the AWS CLI, SDK or AWS Management Console to configure VPC Lattice resources. To learn more, please read the launch blog , Amazon VPC Lattice and Oracle Database@AWS documentation.

Amazon EC2 C8g, M8g and R8g instances now available in Asia Pacific (Singapore)

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Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8g, M8g, and R8g instances are available in AWS Asia Pacific (Singapore) region. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 C8g instances are built for compute-intensive workloads, such as high performance computing (HPC), batch processing, gaming, video encoding, scientific modeling, distributed analytics, CPU-based machine learning (ML) inference, and ad serving. Amazon EC2 M8g instances are built for general-purpose workloads, such as application servers, microservices, gaming servers, midsize data stores, and caching fleets. Amazon EC2 R8g instances are ideal for memory-intensive workloads such as databases, in-memory caches, and real-time big data analytics. These instances are built on the AWS Nitro System .

AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. These instances offer larger instance sizes with up to 3x more vCPUs and memory compared to Graviton3-based instances. AWS Graviton4 processors are up to 40% faster for databases, 30% faster for web applications, and 45% faster for large Java applications than AWS Graviton3 processors.

To learn more, see Amazon EC2 C8g Instances , Amazon EC2 M8g Instances , and Amazon EC2 R8g Instances . To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton . To get started, see the AWS Management Console . 

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