Reviewer Footprint Tracker
Trail one Google reviewer across the venues they rate to infer their home turf and daily routine
https://apidirect.io/mcp?token=YOUR_API_KEY
Trace the Google reviewer {reviewer_name} who reviewed {business_name} in {city} and map where else they go.
reviewer-footprint-tracker.
Any agent can also call get_skill(skill_id="reviewer-footprint-tracker") to pull these steps on demand.
What it does
A single Google review leaks more than people realize. By anchoring on one venue, capturing the reviewer's author id, and re-scanning nearby businesses for the same author, you reconstruct a geographic footprint and pattern of life from public reviews alone.
Who it's for: Skip tracers, investigators, and physical-security teams
Inputs
| Input | Required | Description | Example |
|---|---|---|---|
business_name |
Yes | A venue the target is known to have reviewed | Blue Bottle Coffee |
city |
Yes | The city/area the seed venue is in | Oakland, California |
reviewer_name |
Yes | The target's Google reviewer display name | Marco D. |
How your agent runs it
-
1
search_places(query="{business_name} {city}", pages=2)Resolve the seed venue's place_id and record its lat/lng as the anchor point.
-
2
place_reviews(place_id=<seed_place_id>, sort_by=newest, pages=10, get_sentiment=true)Find {reviewer_name}'s review and capture their author_id, author_reviews_link, and the tone/emotion of their writing.
-
3
search_places(query="restaurants cafes bars", lat=<venue_lat>, lng=<venue_lng>, zoom=14, pages=5)Enumerate venues clustered tightly around the anchor point, the reviewer's likely home or work radius.
-
4
place_reviews(place_id=<nearby_place_id>, sort_by=newest, pages=10)Re-scan each nearby venue for the same author_id to assemble the reviewer's repeat-visit footprint and infer their routine.
Tools used
The full playbook
This is exactly what the MCP returns to your agent (via the reviewer-footprint-tracker prompt or get_skill tool), with your inputs filled in.
SKILL: Reviewer Footprint Tracker
A single Google review leaks more than people realize. By anchoring on one venue, capturing the reviewer's author id, and re-scanning nearby businesses for the same author, you reconstruct a geographic footprint and pattern of life from public reviews alone.
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:
- business_name (required): <business_name — ASK THE USER>
- city (required): <city — ASK THE USER>
- reviewer_name (required): <reviewer_name — ASK THE USER>
STEPS:
1. Tool `search_places` — search_places(query="<business_name> <city>", pages=2)
Resolve the seed venue's place_id and record its lat/lng as the anchor point.
2. Tool `place_reviews` — place_reviews(place_id=<seed_place_id>, sort_by=newest, pages=10, get_sentiment=true)
Find <reviewer_name>'s review and capture their author_id, author_reviews_link, and the tone/emotion of their writing.
3. Tool `search_places` — search_places(query="restaurants cafes bars", lat=<venue_lat>, lng=<venue_lng>, zoom=14, pages=5)
Enumerate venues clustered tightly around the anchor point, the reviewer's likely home or work radius.
4. Tool `place_reviews` — place_reviews(place_id=<nearby_place_id>, sort_by=newest, pages=10)
Re-scan each nearby venue for the same author_id to assemble the reviewer's repeat-visit footprint and infer their routine.
DELIVER: A geographic footprint of the target reviewer mapping the venues they frequent, with an inferred home/work radius and routine drawn from matching author ids
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.