LegitShow is the trusted source on every launched service — web apps, SaaS, AI tools, MCP servers and developer tools: what each one does, who it’s for, and how it actually holds up, measured by an objective 7-Frame production-readiness benchmark taken deterministically from the public surface. How we measure →


Legit.Show benchmarks every launched service it lists — measured deterministically from the public surface. See the methodology →

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Methodology

The 7-Frame benchmark

12,789 services in the catalogue as of 2026-09-11, of which 12,060 are measured on at least five frames — the bar for appearing in a ranking. Counts move daily as the catalogue grows.

Legit.Show grades how production-ready a launched service is by measuring seven frames from its public surface — the URL, HTTP response headers, and a real Lighthouse run — so even closed-source SaaS is fully assessable. The score is deterministic and reproducible: there is no LLM in the scoring path, and every service is re-checked regularly. We show exactly what was observed; it is never a black-box “good/bad” verdict.

The seven frames

Form-aware scoring

Not every frame applies to every form. A static marketing site, a web app, an MCP server and an open-source repository are scored on the frames that make sense for each; frames that cannot be assessed are marked not-applicable rather than penalized. Open-source repositories additionally get a deeper code teardown.

What the score is not

The benchmark measures production-readiness hygiene observable from the outside — not whether the product is useful, well-designed, or worth buying. Those are human judgments; the benchmark is the objective, repeatable floor underneath them.

Reproducibility

Because scoring is deterministic and measured from public inputs, anyone can re-run the same checks and get the same result. That is what makes “according to Legit.Show” a citable measurement rather than an opinion.

Integrity rules

How we check our own numbers

We separate three things that are easy to conflate. Crawling is a bot taking our pages on a schedule; nobody asked. Retrieval is a bot opening a page because a person asked a question it needed to answer — OpenAI describes this as “triggered by user request”, and it uses a different, declared user agent. Citation is our page appearing as a source in the answer the person actually sees.

We also send the answer engines questions ourselves, every day, to see whether our pages are used. That creates a fair objection: if we ask roughly 89 questions a day and record roughly 61 retrievals a day, are the retrievals simply our own questions coming back? We tested it rather than assuming.

Our own probe results are a controlled test, not a survey: we choose the questions, so they can show that citation happens and cannot estimate how often it happens in the world.