AWS Messaging Blog
Use AI agents and the Model Context Protocol with Amazon SES
Amazon Simple Email Service (Amazon SES) delivers a cloud-based email solution that empowers businesses to send emails more efficiently and at a larger scale. Its powerful, scalable platform enables organizations from startups to global brands to send personalized, high-volume email communications while maintaining exceptional deliverability and performance.
Amazon SES caters to a wide range of users, from developers and technical marketing professionals to business communicators. In addition to offering robust programmatic access through APIs and SMTP protocols, Amazon SES provides a comprehensive web console and intuitive dashboards that make email configuration and performance monitoring accessible to users with varying technical backgrounds. Historically, navigating email workflows and configuring advanced email capabilities in Amazon SES has required specialized knowledge, resulting in a learning curve for new users. As seen in many other areas, today’s AI tools can offer more intuitive ways to manage Amazon SES to get the most out of your email communications. We have found, however, that these AI tools occasionally produce inconsistent results, often as a result of the underlying large language model’s (LLM’s) training data.
Recognizing the need for a specialized, service-aware, AI-friendly Amazon SES solution, we are introducing the SESv2 MCP Server, a sample Model Context Protocol (MCP) for Amazon SES. We’ve integrated the SESv2 MCP Server sample with the Amazon SES v2 APIs to provide more precise and reliable AI-assisted use, management, and configuration for Amazon SES.
MCP is an open protocol that enables seamless integration between your AI-powered integrated development environment (IDE) or AI assistant, enriching the capabilities of the AI and enabling you to use Amazon SES using natural language. For more info, see the GitHub repo.
We’ve released the SESv2 MCP Server sample on GitHub and invite current and prospective customers to experiment with it in non-production environments. You can use it with your AI tools to explore ways in which AI can be used with Amazon SES to send emails, check configurations, and review deliverability. We’re interested in learning how you use your AI tools and the SESv2 MCP Server to test out email sending in different services or applications. We’re also curious if new customers find it helpful when configuring and learning about their Amazon SES service. No matter how you use it, we are eager for your feedback, comments, and contributions through the GitHub project’s issues.
Solution overview
You can use the SESv2 MCP Server sample with AI assistant applications like Anthropic’s Claude Desktop. You can also integrate it into MCP-compatible agentic AI coding assistants such as Amazon Q Developer, Amazon Q for command line, Cline, Cursor, and Windsurf. When used as an AI coding assistant, the SESv2 MCP Server sample helps developers add Amazon SES email capabilities to their applications and services using plain, natural language prompting. For recommendations from AWS on how to improve your vibe coding experience, refer to Vibe coding tips and tricks.
After you’ve configured the sample and authenticated with your AWS credentials, you can use natural language in your chosen AI tool. For example, an email marketing manager might want to ask Anthropic’s Claude Desktop “provide me with the status of the verified identities in my SES account, along with any recommendations to improve deliverability.” Someone new to Amazon SES can ask the Amazon Q CLI “create a new Amazon SES configuration set for the octank.com identity, enable it for event publishing for bounces and complaints.” Similarly, the developer of an AI-enabled restaurant booking application might ask the Amazon Q CLI “my application needs to send email confirmation of a customers online booking. Can you walk me thru adding this capability to my app using my SES account?”
As you can see from these examples, although it’s helpful to know a bit about email, and Amazon SES in general, with the help of your AI tool and the SESv2 MCP Server sample, you don’t need to be an email or Amazon SES expert. The combination of your creativity, AI tool, and the SESv2 MCP Server sample empowers even non-developers to create, test, and monitor Amazon SES workflows using natural language.
The SESv2 MCP Server sample release uses the open source Smithy Java project, which is still in development. As such, the SESv2 MCP Server is considered a sample, and we do not recommend employing it for production use. When a stable version is available, we might update this post and the GitHub repository accordingly.
Prerequisites
To follow along with the example use cases, make sure you have the following prerequisites set up:
- AWS credentials with appropriate permissions.
- An MCP-compatible LLM client (such as Anthropic’s Claude Desktop, Cline, Amazon Q CLI, or Cursor). For this post, we use the Amazon Q Developer CLI. For installation instructions, refer to Installing Amazon Q for command line.
- Java 21 (or later) runtime (as required by Smithy Java).
- Access to GitHub.
- Git installed locally. For instructions, see Getting Started – Installing Git.
Best practices for using MCPs
To maximize the benefits of MCP-assisted development while maintaining security and code quality, we suggest you follow these essential guidelines:
- Always review generated code for security implications before deployment
- Use MCP servers as accelerators, not replacements for developer judgment and expertise
- Keep MCP servers updated with the latest AWS security best practices
- Follow the principle of least privilege when configuring AWS credentials
- Run security scanning tools on generated infrastructure code
Configure the AWS CLI
Use the following command to configure the AWS Command Line Interface (AWS CLI) with the AWS credentials for your Amazon SES account and AWS Region:
aws configure
Clone and build the GitHub repository locally
To use macOS or Linux, use the following command to clone and build the GitHub repo:
For Windows, use the following command:
Copy the absolute path to the .jar file (JAR_PATH_FROM_BUILD_OUTPUT). This will be printed at the end of the build script:
/<your path>/sample-for-amazon-ses-mcp/artifacts/sample-for-amazon-ses-mcp-all.jar
Configure your AI tool to use SESv2 MCP Server
When the build is complete, add SESv2 MCP Server to your AI tool’s MCP configuration:
See MCP configuration for configuration steps. See the Claude Desktop MCP configuration guide for setup instructions.
After you build the SESv2 MCP Server and configure your AWS credentials, you’re ready to interact with Amazon SES. Keep in mind that effective, thoughtful prompting is crucial for successful AI-assisted development. For more information about vibe coding, see Vibe coding tips and tricks.
Example use cases
In this section, we provide some guided examples using the Amazon Q Developer CLI to interact with Amazon SES. Feel free to experiment on your own use cases, and share your comments and ideas through the GitHub project’s issues. Do not disclose any personal, commercially sensitive, or confidential information.
Get information, recommendations, and configurations your Amazon SES account
Open your AI tool; for these examples, we use a macOS terminal and initiate a chat session with the Amazon Q CLI:
q chat
We’ve found it useful to provide your AI tool with some guidance:
You're connected to the SESv2 MCP Server and have access to the AWS SESv2 APIs.
Ask the Amazon Q CLI about your AWS account’s SES email identities:
Tell me about the identities in my account, and also if the account is in the SES sandbox?
The Amazon Q CLI will request permission to use the SESv2 MCP Server (which provides the Amazon Q CLI with the SESv2 APIs ListEmailIdentities and GetAccount) to query your AWS SES account and reply with a detailed summary.
Ask the Amazon Q CLI if it has any recommendations related to improving deliverability for your Amazon SES account:
Do you have any recommendations to improve email deliverability for my SES account?
The Amazon Q CLI will use the SESv2 MCP Server (which provides the CLI with the SESv2 API ListRecommendations) to query your Amazon SES account and reply with a detailed summary.
Ask the Amazon Q CLI to set up Amazon SES click tracking for one of your domains. We have found it helpful to remind the CLI that it has access to additional knowledge of the AWS service APIs. It’s also a good idea to make sure the AI tool doesn’t invent nonexistent APIs.
Send emails with your Amazon SES account
Using its knowledge of Amazon SES from the SESv2 MCP Server and permissions to use your Amazon SES account (aws configure), you can use your AI tool to create and send emails using Amazon SES.
If your Amazon SES account is in the Amazon SES sandbox, you are limited to sending and receiving email from verified email addresses. You are also limited to 200 messages in 24 hours. For more information about the Amazon SES sandbox, see Request production access (Moving out of the Amazon SES sandbox). If you’re in the sandbox, you can simply ask your AI tool “verify my email address <email@myaddress.com>.”
Ask the Amazon Q CLI to send a test email with a sample HTML body:
Check your email, where you will receive a response.
You can get creative and ask the Amazon Q CLI to create a formatted email template with personalization using a simple table with email recipients, the product they bought, and their postal code:
As we’ve demonstrated, you don’t need to be a seasoned developer to create and test Amazon SES workflows when you have an AI tool and the SESv2 MCP Server sample.
Conclusion
The SESv2 MCP Server sample democratizes the ability to configure, manage, and create sophisticated email automation workflows with Amazon SES.
The examples and guidance in this post demonstrate how even newcomers can use AI tools like the Amazon Q CLI to test out configuring, monitoring, and sending emails with Amazon SES using natural language. More technical users, including developers, can use the SESv2 MCP Server sample to build and test intelligent email applications that use Amazon SES, or to test out building Amazon SES sending into their own application.
We hope you will experiment with the SESv2 MCP Server sample and provide us with your thoughts and feedback, and perhaps contribute to the project through the GitHub project’s issues.