Question 1

How does this scanner differ from classic Google SEO audits?

Accepted Answer

Traditional SEO focuses on keyword density and snippet CTR. AI agents (ChatGPT, Claude, Perplexity) operate via RAG pipelines: They extract facts from raw server HTML, rely on explicit JSON-LD knowledge graphs, and prioritize token-efficient formats such as /llms.txt and markdown.

Question 2

Why do many websites inadvertently block AI search crawlers?

Accepted Answer

Standard Web Application Firewalls (WAFs) and outdated robots.txt rules frequently misclassify user-agents like GPTBot as scraping attacks. This leaves AI engines unable to fetch the domain, causing them to reference competitors instead.

Question 3

What benefits do /llms.txt and markdown negotiation deliver?

Accepted Answer

An /llms.txt file provides an LLM with a compact, token-efficient table of contents of your core offerings. Dynamic markdown negotiation serves plain text without HTML overhead, minimizing latency and parsing hallucinations.

Question 4

How can cyberEiche assist in fixing discovered agent barriers?

Accepted Answer

cyberEiche implements turnkey agent optimization: We configure robots.txt and Content Signals, generate compliant /llms.txt files, enrich Schema.org data, and implement cache-safe markdown negotiation on your servers.

AI Agent & LLM Visibility Scanner

Inspection Methodology & Standards

Crawl Permissions & WAF Filters

Structured Meaning & Schema

Agent-Native Formats

WebMCP & In-Browser Tools

Frequently Asked Questions about the AI Visibility Scanner