The Model Context Protocol (MCP) makes building intelligent integrations between large language models (LLMs) and cloud services easier. By combining MCP with Azure Functions, developers can expose serverless tools that LLMs can securely call in real time. This article describes how to set up Azure Functions as a custom MCP server, extend it with custom tools, and connect it to clients such as VS Code in Agent Mode.

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AWS now allows automatic deletion of associated Amazon EBS snapshots when deregistering EC2 AMIs, reducing storage costs and simplifying cleanup. Previously, snapshots had to be manually deleted, risking orphaned resources. This feature is long overdue. In a previous post, I introduced a Bash script that deleted the snapshot when an AMI was deregistered. The script should still work, but now you can automatically delete snapshots in the AWS Console and the AWS CLI.

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Mountpoint for Amazon S3 (mountpoint-s3) now allows you to mount an S3 bucket in AWS at EC2 instance boot time via fstab. However, it remains non-POSIX compliant, so standard filesystem operations like deleting or renaming files still do not work. In contrast, the open-source alternative s3fs-fuse offers substantial POSIX support but comes with its own drawbacks.

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Creating an AI agent in Amazon Bedrock involves a relatively complex process. The example in this post has been simplified for clarity. Once you have your first Agent setup working, exploring all the features that AI agents in AWS offer becomes much easier. The Bedrock Agent I discuss uses a Lambda function, which can be triggered by the AI model when the user prompt indicates its usefulness. An AWS Lambda function is a serverless compute service that runs your code in response to events without provisioning or managing servers. These functions may involve complex logic, enabling the AI agent to perform sophisticated tasks autonomously.

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