CrawlCheck Skip to content

Menu

For agents

The door: what an agent should know before it acts on a site

Quickstart: ask before you act

MCP, curl, Python, OpenAI Agents, Claude tool use, LangChain

The signed answer about any domain, laid out for a person

How each named agent behaves on sites, from verified requests

Capability registry

What sites declare agents can do, and what was verified

Verify a record

Check any answer or bundle on your own machine

Before you act

One signed answer, no key.

Read, cite, connect or transact: ask first, verify the receipt offline, keep it with the action.

Start in a minute

For sites

The door: scan free, have it fixed, keep it fixed

The checks behind the audit, one at a time

Is your business record right where agents look?

Compare two sites

Two domains, side by side, same measurement

What search and answer engines read from a video

Browser extension

The second vantage point: from your own address, after JavaScript

The record on your phone, with receipts

Your site

Measured as agents see it.

A free A–F audit with every defect named, no account. Fix it, then prove it held.

Developers

The door: the API by lane, the specs, SDKs, keys and quotas

Every endpoint, with a worked example from production

Resolve protocol

The signed answer: what it holds, how to verify it

SDKs: npm and PyPI

Typed clients, the offline verifier, conformance fixtures

The Ed25519 keys every answer is signed with

Every finding the measurement can raise, with its revision

Run the suite against your own implementation

Data

The door: census, conduct, proof and datasets, each with a denominator

Signed decisions, scans and crawler visits, counted

Proof and timestamps

How every record is sealed and anchored

CC BY 4.0 snapshots and the archive

SEO and AI tools, measured

The vendors that measure sites, measured by the same rules

Registry: media kits, llms.txt, entity graphs

Verified machine-readable profiles of real organisations

Learn

The door: guides, findings and the glossary, from measurements

The scan, stage by stage, and what each grade means

How to check one thing, with the rate at which it fails

Dated cases from real scans, with the bytes behind them

Every term the product uses, defined

Who sends each agent, what it reads, how to verify it

Pricing Sign in

Glossary · Answer engines and retrieval

hallucination

A confident statement a model generates that is not supported by any source.

What hallucination means

Distinct from hallucinated attribution, where the claim is right but the credited source is wrong.

Terms this definition uses

hallucinated attribution

Related terms in Answer engines and retrieval

How a generated answer gets made, and what your page is competing for inside it.

AEO · GEO · AI visibility score · share of voice · prompt set · brand mention · RAG · grounding · training data · embedding · chunking · context window · token · refusal · AI Overview · answer engine · citation · retrieval · hallucinated attribution · Hybrid search · Re-ranking · Passage ranking · Query fan-out · Chunk boundary · BM25 · cross-encoder · AI Mode · information gain · dense retrieval

Questions about hallucination

What is hallucination?

A confident statement a model generates that is not supported by any source.

What does hallucination not show or guarantee?

A confident statement a model generates that is not supported by any source.

Which area of the glossary does hallucination belong to?

Answer engines and retrieval: How a generated answer gets made, and what your page is competing for inside it.

← token · refusal →

See it in the full glossary · 731 terms across 19 areas.