Recommendations MCP tools add algorithms, placement, and analytics
Your AI assistant can now create Recommendations boxes from best-practice algorithms, change settings and placement, and rank boxes by clicks and revenue. Find the weakest box and fix it in the same conversation.
The Recommendations tools in the Hello Retail MCP server now cover algorithms, settings, placement, and analytics. Your AI assistant can create Recommendations boxes, change how they pick products and where they appear, and tell you which ones earn their place.
Why it matters
The analytics and the Recommendations tools work together. Ask which box converts worst, look at its design and placement, and draft an improvement, all in one conversation.
What you can ask for
- New boxes from proven algorithms. List Hello Retail’s best-practice algorithms for a page type and start a box from one.
- Algorithms and settings. Read the steps behind a box, change product count, devices, or name, and move it to a different spot on the page.
- Analytics. Site-wide totals, the best and worst boxes, and one box’s day-by-day views, clicks, conversions, and revenue. Recommendations you render yourself through the API are covered too.
A good first request:
Which of my Recommendations boxes have the lowest click-through rate this month? Suggest what to change on the worst three.
Good to know
Edits to a live box leave it in draft, so the live version keeps serving until you publish from My Hello Retail.
Every tool, with ready-to-paste prompts, is in the developer docs: Recommendations and Recommendations analytics.
