The Scenario
You manage healthcare marketing for a regional clinic network. Your team has 150 clinic locations listed in column A of a Google Sheet—city names, one per row. It's early February. The allergy season campaign for grass, tree, and weed pollens launches in six weeks, and the media buyer is asking which markets should get the heaviest spend in the first wave. To answer that, you need current pollen risk levels at each site—not national averages, actual hyperlocal readings.
The bad version:
- Navigate to Ambee's pollen endpoint documentation, find the query parameters for grass, tree, and weed pollen separately, authenticate your API key in Postman, run the first city.
- Extract the
riskfield from three separate JSON sections, open the sheet, paste into columns B, C, and D, navigate back to Postman, type the next city name. - After the sixth city, realize the "weed" risk you pasted for row 3 actually came from the row 4 query and the data is misaligned by one row for everything below it.
Allergy campaign timing is exactly the kind of decision that depends on comparing 150 markets simultaneously, not reading them one at a time over the course of an afternoon while trying not to lose your place.
The Easy Way: One Prompt in SheetXAI
SheetXAI sits inside your Google Sheet as an AI agent. It reads your city list, calls the Ambee pollen API for each location, and writes the risk levels back into the columns you specify—all in one run.
Here is the prompt for this task:
For each city in column A, fetch the current pollen levels (grass, tree, weed) and risk ratings from Ambee and write the results into columns B–G
What You Get
- Column B: Grass pollen count
- Column C: Grass pollen risk rating (Low / Moderate / High / Very High)
- Column D: Tree pollen count
- Column E: Tree pollen risk rating
- Column F: Weed pollen count
- Column G: Weed pollen risk rating
- Cities Ambee cannot resolve return "No data" across B–G rather than leaving the row blank
What If the Data Is Not Quite Ready
The city column includes state abbreviations mixed in (e.g., "Austin TX" or "Austin, TX")
Parse the city names in column A, strip state abbreviations, fetch current grass, tree, and weed pollen counts and risk ratings from Ambee, and write results into columns B–G. Flag any city that doesn't resolve in column H.
The team wants only markets with at least one pollen type rated High or Very High
Fetch current grass, tree, and weed pollen risk from Ambee for each city in column A, write results in columns B–G, and create a filtered view on a new tab called "Priority Markets" containing only rows where any pollen type is rated High or Very High
Pollen data needs to be joined with the campaign budget column already in column B
The campaign budget is already in column B. Fetch current grass, tree, and weed pollen counts from Ambee for each city in column A and write pollen data starting in column C—grass count in C, grass risk in D, tree count in E, tree risk in F, weed count in G, weed risk in H—without touching column B.
Kill-chain: parse cities, fetch pollen, score markets, sort for campaign planning
For each city in column A, strip any trailing state abbreviations, fetch current grass, tree, and weed pollen risk from Ambee and write into columns B–G, compute a composite "Pollen Pressure" score in column H as a weighted average (grass 30%, tree 40%, weed 30%), then sort the sheet by column H descending so the highest-pressure markets appear first
Run the whole sequence in one ask rather than staging each step separately.
Try It
Pull up any Google Sheet with a clinic or location list and get the 7-day free trial of SheetXAI. Ask it to fill pollen risk columns from Ambee for every city in column A, and your media buyer has the market prioritization data they need without a single API call on your end. Also useful: checking wildfire status for coordinates or the Ambee hub for all environmental data use cases.