Implementation checklist

AI crawlers rely on robots.txt to know they are allowed. Missing or restrictive rules silently remove you from AI search results.

Without llms.txt, LLMs have no structured way to understand which content you want them to use.

Agents use meta descriptions as a first signal for page intent. Missing OG tags leave link-preview agents without structured context.

If core content renders only client-side, AI crawlers miss it entirely. This is a common hidden blocker.

A canonical pointing at another domain silently redirects LLM crawl credit away from your site.

Organization schema anchors facts to your brand entity across sources; without @id linkage, your brand is only a string.

Each Q&A becomes a directly quotable structured fact that AI systems can extract and surface.

Shopping agents need complete product facts such as name, price, availability, image, and description.

Free Listings must be activated explicitly; without them, products miss AI-powered shopping surfaces.

RSS is a lightweight freshness and discoverability signal for LLM training pipelines and AI agents.

Statistics, direct quotations, cited sources, and fluent prose are the content signals that earn AI citations.

Wikipedia and Wikidata let AI systems anchor facts to your brand entity, even where knowledge is sparse.

Without a custom channel group, AI referral traffic is misattributed and GEO progress cannot be measured.

Stale links confuse LLMs fetching your content and make the site look unmaintained.

Cold recall is the primary signal of long-term GEO progress: whether an LLM knows your brand without retrieval.

Annotated forms become agent-discoverable tools without requiring a JavaScript integration.

A WebMCP manifest makes the tools your site offers machine-readable before agents crawl every page.

x402 and BuyAction are emerging rails for agent-initiated purchasing and early catalog positioning.

Tool descriptions and model behaviour change; recurring review keeps the agent surface usable.