Using MCP on AWS: Now Available with Amazon Q

Written by Minhyeok Cha

There have been a couple of new developments regarding Q.

One is multilingual support (including Korean),

and the other is the combination of CLI+Q+MCP.

MCP is hot again these days.

As someone working in IT, I felt I should at least check what this thing is about, albeit late, so I wrote this article.

While it's typically installed and used with things like Claude or Cursor, as an employee of an AWS partner company, I'll try to integrate it with AWS.

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Table of Contents

1. What is the Model Context Protocol (MCP)?

2. Installing Amazon Q and SSO Sign-Up

3. Applying the MCP Server

4. What can we make MCP do?

5. An extra part I'm adding because I feel it's lacking

6. Wrap-up

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What is the Model Context Protocol (MCP)?

You might already know this, but let's briefly explain it and fill out the content anyway.

The Model Context Protocol (MCP) is an open protocol that supports seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs to the necessary context.

In fact, various MCP servers are already available on GitHub, so you can simply grab one and use it. If you find it lacks desired features, implementing your own is also an option.

Refer to the following link for GitHub servers → → https://github.com/modelcontextprotocol/servers

The MCP flow we'll use looks roughly like this. As mentioned earlier, we'll proceed using CLI and Q.

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Amazon Q Installation and SSO Sign-Up

1.First, sign up for SSO.

[Q] How to Subscribe to Amazon Q Developer Pro

2.Follow these steps to sign up.

Note: If you are using an AWS Organization and testing with an account under it, this is treated as an account instance and incurs the following Q limitations.

3.Next is installing Amazon Q on the CLI.

Follow these steps for installation → [Link]

4.After that, open the terminal and type “q chat”. The screen should display as follows.

Korean support was essential for me, who struggles with English, and fortunately, it's possible. :)

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Applying the MCP Server

While there are various MCP servers, for this article, I sought the easiest, least troublesome, and most visually clear server for testing and selected the Puppeteer MCP server.

It's a Model Context Protocol server that uses Puppeteer to provide browser automation capabilities. While it's described as an MCP server that lets LLMs interact with web pages, take screenshots, and execute JavaScript in a real browser environment, let's see what kind of output it produces in the test results below.

Visit the GitHub repository I posted earlier. For your desired server, you'll find the server configuration method, which is also very simple.

~/.aws/amazonq

Navigate to the amazonq configuration directory in your terminal, create a “mcp.json” file, and simply paste in each server's configuration values. Then, reboot the CLI Q you started earlier.

💡To reboot, exit with /quit and then run it again.

If “Puppeteer loaded” appears above, MCP serves is successfully applied.

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What should we have MCP do?

After running Puppeteer a few times, I found I could easily write and use something like this:

• Capture web page screenshots

• Retrieve web page content

• Interact with web pages

I asked it to summarize one of the blog links that helped me study MCP, using one of these simple commands.

It output the following content.

By granting permissions to this tool, the MCP reads and organizes the link for you.

Summarize MCP-related blog content

Hmm... But it feels a bit anticlimactic. So this time, instead of just a URL, I fed it a PDF I'd previously organized about personal information and requested an analysis related to ISMS-P. The results were surprisingly good.

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An additional part done out of disappointment

Since this MCP only uses URLs, it threw an error saying it couldn't find content opened as a PDF.

It seems to be an issue with the browser directly rendering the PDF.

그러더니 해당 문제를 인지하고 curl 명령어를 사용하여 PDF 파일을 로컬에 다운로드했습니다.

I used the ls -la command to verify the downloaded PDF file existed and confirmed it downloaded correctly.

I attempted to read the PDF using Python's PyPDF2 library, but it wasn't installed.

After creating a Python virtual environment and installing the PyPDF2 library, I successfully read the PDF file and extracted the text.

While the subsequent details are confidential and cannot be shared, the CLI + Q functionality on this blog appears to be sufficiently verified.

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Conclusion

Upon finishing this post, I noticed that AWS's official YouTube channel already has a video about using the Developer Q CLI and MCP server. It's a bit disappointing that if I had acted a little faster, I could have posted about the Q CLI and MCP first.

MCP truly unlocks diverse possibilities. Integrating MCP with existing servers enabled AI models to access required endpoints with appropriate permissions, marking a significant leap forward in development automation and efficiency.

💡Of course, careful consideration is required when adopting MCP. Issues like the scope of permissions granted to the MCP server, logging, and patch management associated with operating your own server are currently significant topics of discussion within the security community.

My first experience using Developer Q CLI was astonishing in itself. Even without MCP's additional features, its intelligent support—proposing and executing necessary library installations or version patches based on the situation—completely transformed my development workflow.

In the past, when execution errors occurred, I had to check logs and manually search for solutions one by one. Now, I can resolve issues much more efficiently without that hassle.