AI Reports on Email Campaigns

A marketing team at a hospitality group sends several email campaigns every week, and every week someone writes a report about them: what went out, which offer each email promoted, how it looked, and which call to action it used.

They tried handing the job to an AI assistant. It could read each email’s HTML, but it couldn’t see the email. The hero photo, the logos and most of the design are images loaded from the web, and to the assistant they were just links. It couldn’t tell whether the hero image worked, whether the layout held together, or what the subscriber actually saw.

How it works

With the ScreenshotOne MCP server, the assistant renders each email before it writes about it:

1. You give the assistant the email’s HTML, from your email platform’s export or a campaign file.
2. The assistant calls the render-html-screenshot-as-file tool with the HTML and a viewport_width of about 700, the width emails are designed for.
3. ScreenshotOne renders the email with its images, fonts and styles and returns the screenshot right in the response, so the assistant sees it immediately, with nothing to download.
4. The assistant describes each email, compares it with the previous weeks and writes the report, with the screenshots to back it up.

A prompt can be as simple as:

1
Render each of these email HTML files with ScreenshotOne's
2
render-html-screenshot-as-file tool at viewport_width=700. For each email,
3
summarize the headline, the offer, the hero image and the call to action,
4
then write this week's campaign report.

The tool works in ChatGPT, Claude, Cursor and other MCP clients. See the MCP documentation for every option.

What the team gets

• A report written from what subscribers saw: the assistant judges the real design, not the markup.
• Hours back every week: no more taking screenshots of each email by hand and pasting them into a document.
• A visual archive: each week’s report keeps the screenshots, so you can see how campaigns changed over time.
• Faster reviews: the same screenshot catches a broken image or a missing call to action before the next send.

From a chat to an automation

Starting in a chat is the quickest way to try it. When the report becomes a routine, send the HTML to the API directly with its html option in a JSON POST request, so nothing has to be pasted into the chat. You can also do it without code from n8n, or with a short script from your own code, then hand the images to your AI model for the write-up.

The same approach works for any HTML that an AI assistant needs to see: newsletters, social cards, invoices and report templates. See the HTML to image API for more.