We read the reviews, run the stats, and tell you whether the rating is earned, with the evidence to back it up.
We don't scrape Google. Your AI agent already has a browser; we have the scoring. It collects a listing's reviews, we grade them, and the place appears here for everyone.
check a place with your ai agent
1.
Connect the legitreviews MCP. In Claude Code:
claude mcp add --transport http legitreviews https://legitreviews.ai/mcp
In claude.ai: Settings → Connectors → Add custom connector → https://legitreviews.ai/mcp. Any other MCP client (Muse, Cursor, your own agent) takes the same URL, Streamable HTTP, no key needed to try it.
2.
Ask it about a place:
“Are the Google reviews for the Blue Lantern Cafe on Main Street legit?”
4.89★ claimed4.61★per established accounts · 3,401 reviews
Unusually relentless volume: 3.1 reviews/day with reviews on 71% of all days, beyond any organic place we've verified.
24% of all reviews (833 of 3401) landed on 8 burst days. A surge this large reads as a review campaign even when most accounts look real, such as an in-person solicitation program or sophisticated review farm.
8 reviewers independently report the business rewards customers for reviews: "I did review mostly for one free topping but it was honestly a good dinner" · "They offered a free drink for a Google review, which might explain the overwhelmingly positive reviews here."
base score
every place starts at 100
100
rating inflation
headline 4.89★ is 0.28 above the 4.61★ that established accounts (30+ lifetime reviews) give
-17
sustained drip-feed volume
3.1 reviews/day with reviews on 71% of all days over 36 months. Organic places in our verified set stay under ~1.2/day even at 4000+ reviews
-8
coordinated review injection
269 reviews (7.91% of all) from low-history, non-Local-Guide accounts, across 9 statistically-impossible days. The worst, 2023-11-30, drew 57 reviews (about 1 in 1050), 36 of them injected; 253 of them predate the 24-month window and weigh a third
-6
review solicitation
8 reviewers independently report the business rewards customers for reviews, e.g. "I did review mostly for one free topping but it was honestly a good dinner", "They offered a free drink for a Google review, which might explain the overwhelmingly positive reviews here."
Reviewers describing the business asking for, steering, or rewarding reviews, classified from the review text by an LLM. Only firsthand reports of policy-violating behavior (rewards, positive-only asks, gating, pressure) are counted. The penalty slides with the number of independent witnesses, so a single account only nudges the score. Tap a reviewer to read their full review.
★★★★★Van Vu · local guide · 19 reviews · a year ago
↳ “I did review mostly for one free topping but it was honestly a good dinner” reward offered · counted
★★★☆☆고민이고민이다 · local guide · 116 reviews · 2 years ago
↳ “They offered a free drink for a Google review, which might explain the overwhelmingly positive reviews here.” reward offered · counted
↳ “If you leave a Google review, you get free toppings (the more you spend, the more toppings you get)” (translated from “đánh giá google thì đc tặng phần topping ( đi bao nhiêu tặng bấy nhiêu)”)reward offered · counted
↳ “The restaurant still has a Google Maps review program that gives free toppings” (translated from “quán vẫn còn chương trình đánh giá google maps tặng toppings”)reward offered · counted
↳ “Currently has a promotion where you can eat and try for free” (translated from “đang có chương trình đánh giá được ăn thử miễn phí”)reward offered · counted
↳ “Shoday ramen is currently running a free trial evaluation program” (translated from “Shoday ramen đang có chương trình đánh giá dùng thử miễn phí”)reward offered · counted
↳ “Shoday Ramen is currently offering a free trial evaluation program” (translated from “Shoday Ramen đang có chương trình đánh giá dùng thử miễn phí”)reward offered · counted
↳ “Currently has a free trial evaluation program” (translated from “đang có chương trình đánh giá dùng thử miễn phí”)reward offered · counted
evidence: burst days (9)
Days with statistically-improbable review volume for a place averaging 3.13/day. Flagged = reviews posted that day by low-history, non-Local-Guide accounts: a statistical flag, not proof they’re fake (a real group visit or promo can look identical). Days that kept the place’s normal share of Local Guides read as genuine surges.
day
flagged
odds
2025-10-07
16 of 21
1 in 1011
2023-12-31
19 of 20
1 in 1010
historical
2023-12-03
41 of 48
1 in 1039
historical
2023-12-02
38 of 48
1 in 1039
historical
2023-11-30
36 of 57
1 in 1050
historical
2023-11-29
28 of 37
1 in 1026
historical
2023-11-28
37 of 53
1 in 1045
historical
2023-11-27
30 of 44
1 in 1034
historical
2023-10-25
24 of 32
1 in 1021
historical
organic: kept the place’s normal share of Local Guides, reads as a genuine surge. historical: older than 24 months. The odds of each day are in the downloadable report.
how it works
1
Step 1: your agent collects the reviews
It opens the listing in its own browser, sorts by newest and collects them: all of them, or the 500 most recent at a big listing. We don't scrape Google.
A to F, plus the rating credible reviewers actually give, with the evidence one click away. The place then appears here for everyone.
Google usually asks the browser to be signed in before it will sort or load more reviews. The agent reads Maps through your signed-in browser, much like scrolling the listing yourself. Heavy use could trip Google's rate limits; if you plan to check many places, a secondary Google account is a sensible choice.
No agent? Scripts can POST the same review JSON to /api/submissions; the format is at /api/sources/google-maps. Everything required is visible on the review card; review ids and exact timestamps are optional. Results from a single unconfirmed submitter are labeled as such until a second source agrees.
methodology
what signals do we look for?
We read a place's full review history and weigh the signals that are costly for a review farm to fake:
Burst campaigns: clusters of 5 star reviews dumped on a single day by low-history accounts. A genuine surge keeps a place's normal share of Local Guides, so it isn't counted against it.
Drip-feed farms: reviews arriving nearly every day at a pace no organic business sustains, a paid stream spread thin to dodge obvious spikes.
Cross-posted text: the same review copied word-for-word onto other businesses, a farm seeding a whole chain.
Throwaway accounts: single-review profiles padding the average up.
Review solicitation: reviewers themselves reporting that the business rewards, pressures, or steers reviews (“free dessert if you leave 5 stars”). An LLM reads every such mention; only firsthand reports of policy-violating practices count, and the penalty deepens with each independent witness. Politely asking for a review is allowed by Google and never counted.
Too-perfect records: a spotless, unanimous 5 star history often signals curated or incentivized reviews, so it can't earn a top grade, even when we can't prove fraud.
what's the “adjusted rating”?
What hard-to-fake reviewers actually give, vs the headline number. A gap means the rating is inflated. Two groups qualify:
Credible reviewers are Local Guides who also wrote a substantive review. The badge alone is gameable (farms run Local Guide accounts that drop bare 5 star ratings), so we only count ones that put real words behind the stars.
Established accounts have 30+ lifetime reviews, regardless of badge or text. Farms pad accounts with a handful of reviews to look plausible, but aging thousands of accounts past 30 is the expensive part.
Each place is anchored to whichever group gives the more conservative rating. When a place corrects the headline number, the “per credible reviewers” / “per established accounts” label tells you which.
where does the review data come from?
From the agents and scripts that submit it. Every submission is checked for structure and consistency (Google's own review and profile ids, timestamps against displayed dates, the rating average against the displayed rating, the count against the listed count, overlap with what we already hold), and a place submitted by one unconfirmed source carries an unverified source note until a second, independent submission agrees. Places we pulled ourselves before October 2026 need no such note.
what does a high score mean?
It means we found no evidence the business is manipulating or inflating its own reviews. It does not promise the rating is accurate: a clean place can still be mis-rated by too few reviews, reviewers who self-select, or honest reviews that just don't match your experience. We measure whether the number looks gamed, not whether it's right.