Geo-Trend Divergence Radar
Catch a topic breaking in one region before it goes global by diffing live trend lists across markets
https://apidirect.io/mcp?token=YOUR_API_KEY
Find topics breaking in {region_a_woeid} that haven't hit {region_b_woeid} yet and tell me which to jump on
geo-trend-divergence-radar.
Any agent can also call get_skill(skill_id="geo-trend-divergence-radar") to pull these steps on demand.
What it does
Trends surface in one geography first. By set-differencing two regional trend lists and reading the driver posts, you spot a topic surging in market A that hasn't hit market B yet — an early-mover window.
Who it's for: Trend forecasters, social strategists, and content teams
Inputs
| Input | Required | Description | Example |
|---|---|---|---|
region_a_woeid |
Yes | WOEID of the lead market to scan for emerging trends | 23424977 (United States) |
region_b_woeid |
Yes | WOEID of the comparison market to diff against | 23424975 (United Kingdom) |
How your agent runs it
-
1
twitter_trends(woeid={region_a_woeid})Pull the live trend list for the lead market and capture each trend name and tweet volume.
-
2
twitter_trends(woeid={region_b_woeid})Pull the comparison market's list and set-difference it to isolate trends present in A but absent in B.
-
3
search_twitter(query=<divergent trend>, sort_by=most_recent, pages=3, get_sentiment=true)Read the freshest posts behind each divergent trend to identify the driver and whether sentiment is positive momentum or backlash.
-
4
search_twitter(query=<divergent trend>, sort_by=relevance, pages=2)Pull the highest-engagement posts to estimate amplification potential and rank which divergent topics to act on first.
Tools used
The full playbook
This is exactly what the MCP returns to your agent (via the geo-trend-divergence-radar prompt or get_skill tool), with your inputs filled in.
SKILL: Geo-Trend Divergence Radar
Trends surface in one geography first. By set-differencing two regional trend lists and reading the driver posts, you spot a topic surging in market A that hasn't hit market B yet — an early-mover window.
You are running this skill on API Direct via its MCP tools. Execute the steps below yourself by calling the named tools in order — values in <angle brackets> come from a previous step. Then deliver the result described at the end.
INPUTS:
- region_a_woeid (required): <region_a_woeid — ASK THE USER>
- region_b_woeid (required): <region_b_woeid — ASK THE USER>
STEPS:
1. Tool `twitter_trends` — twitter_trends(woeid=<region_a_woeid>)
Pull the live trend list for the lead market and capture each trend name and tweet volume.
2. Tool `twitter_trends` — twitter_trends(woeid=<region_b_woeid>)
Pull the comparison market's list and set-difference it to isolate trends present in A but absent in B.
3. Tool `search_twitter` — search_twitter(query=<divergent trend>, sort_by=most_recent, pages=3, get_sentiment=true)
Read the freshest posts behind each divergent trend to identify the driver and whether sentiment is positive momentum or backlash.
4. Tool `search_twitter` — search_twitter(query=<divergent trend>, sort_by=relevance, pages=2)
Pull the highest-engagement posts to estimate amplification potential and rank which divergent topics to act on first.
DELIVER: A ranked shortlist of topics trending in the lead region but not yet in the comparison region, each with its driver, sentiment, and an early-mover recommendation.
Note: each underlying tool call is billed at its normal endpoint price; get_sentiment adds a small per-page surcharge. Page through results as needed but stop once you have enough to deliver the outcome.