Agent Readiness — Can an Agent Reach, Navigate, and Parse Your Site?
AI visibility work splits into two layers: what your content says (the rest of this skill) and whether an agent can get to it at all. This reference covers the second layer — the access/discovery/parseability audit — plus the emerging shift from agent-readable to agent-actionable sites.
Two free scoring tools shipped in August 2026 and turned this into a measurable discipline:
| Tool | Run it | Method |
|---|---|---|
| Is Agentic (Vercel + Ora) | npx is-agentic yourdomain.com or is-agentic.com |
100+ checks; Essential checks carry most of the score; Recommended checks activate only when evidence shows you have that surface (API, MCP server, commerce); not-applicable checks are excluded, not failed; includes an observed agent journey showing where a real agent hit friction |
| Frase Agent Readiness Checker | frase.io/tools/agent-readiness | Access / Discovery / Parseability triad; 80+ = agents can reliably use the site, 60–79 = solid with gaps, <60 = real access problems |
Run one before and after any agent-readiness work — the score is a shareable artifact and the failed checks are your worklist. (Both are vendor tools with a product behind them; the checks are the value, not the pitch.)
The three questions
1. Access — can an agent get to the page and see real content?
- Core content in the initial HTML response. Most agents never execute JavaScript. If the content only exists after client-side rendering, it doesn't exist. This is the #1 essential check in both tools.
- No bot challenge or firewall block on the intended discovery path. Blocking
OAI-SearchBot,PerplexityBot, orClaude-SearchBotmay reduce search visibility. Blocking model-development crawlers such asGPTBotorClaudeBotcan be an intentional training opt-out and is not, by itself, a visibility failure. Audit what your CDN/WAF actually does to each user agent — many sites block them by default without anyone deciding to. - Correct HTTP behavior: real status codes (no soft-404s), stable canonical URLs, recoverable errors.
2. Discovery — do your files tell agents what's here?
- robots.txt with an explicit AI-crawler stance — name the relevant user agents and state a purpose-specific policy, rather than leaving it to be assumed (see the bot-access guidance in SKILL.md).
- A sitemap that loads and parses cleanly.
- llms.txt at the domain root (see Machine-Readable Files in SKILL.md).
llms-full.txt— the newer companion: your entire site content in one file, so an agent gets everything in a single request instead of crawling. Emerging, cheap to generate alongside llms.txt, and scored as bonus signal by both tools.- robots.txt content-usage statements — an emerging convention for declaring what AI may do with your content (train / cite / summarize), so the answer comes from you instead of being assumed.
3. Parseability — once there, can the agent tell what the page is?
- Valid, substantive structured data (JSON-LD — see the
schemaskill). - A Markdown representation of the page. This is the newest technique in the stack, two implementations:
- Content negotiation: serve compact Markdown at the same canonical URL when the request asks for
Accept: text/markdown, with aVaryheader keeping the HTML and Markdown cache entries separate. (This is how Is Agentic serves its own reports — agents get Markdown, browsers get HTML, one URL.) - Link header: an HTTP
Linkheader on the HTML page pointing to a parallel Markdown version — discoverable without guessing URLs.
- Content negotiation: serve compact Markdown at the same canonical URL when the request asks for
- Clear document structure — one H1, headings that answer sub-questions, extractable answer blocks (the content-patterns reference).
Emerging: agent-actionable, not just agent-readable
Reading is becoming table stakes. The next race is whether an agent can act on your site — fill the form, book the meeting, start the trial. WebMCP is the emerging standard here: a page declares its forms and CTAs as callable tools with input schemas, so an agent doesn't have to reverse-engineer your UI. Early days (label: emerging, not yet a ranking/citation signal), but the direction is clear — if agents are becoming buyers, the site that exposes "start trial" as a structured action wins the agent-mediated conversion that a pretty button loses.
Practical today: make sure your highest-intent actions (signup, pricing, demo booking, contact) work without JavaScript-only flows, have labeled semantic form fields, and return machine-readable confirmation.
Ways to add WebMCP (all early; confirm browser and assistant support before promising anything):
- Implement it yourself. Follow the WebMCP draft to declare each action as a tool with an input schema, wired to the form or API the page already uses. Keep the tool list short (the 2–4 highest-intent actions) and reuse existing validation, spam protection, and permissions rather than building a parallel path.
- Use your platform's support, if it has any. Some site platforms and agent workflows add WebMCP for you. Ploy ◆, for example, has a Ploybook that inspects the site, defines tool contracts, implements them against existing form logic, and validates them. It's a guided workflow, not a hosted MCP server. ◆ marks a Verified Partner of this repository (disclosure, not endorsement); name an alternative alongside it when you mention it.
- Have a coding agent do it in the site's repo, with the same scoping: a few actions, existing logic, a test for each.
Whichever path, test that each tool rejects bad input, that submissions land where the normal form's do (CRM, inbox, conversion tracking), and that nothing bypasses consent or rate limits.
For products an agent installs mid-task (developer tools, APIs, infrastructure), the next step past acting on your site is being the vendor the agent picks in the first place. See agent-defaults.md.
Citation-source volatility (why you diversify)
Third-party citation mixes are not stable — they shift overnight with model and retrieval updates, and August 2026 provided the case study: ChatGPT's query fan-out changes nearly wiped Reddit as a citation source within days (practitioner-reported by multiple AEO teams; one had been earning 24-hour citations from Reddit at 1M+ impressions/month before the change). Meanwhile the same practitioners report business-owned websites dominate Gemini citations (~60%).
LinkedIn moved in the same update: ChatGPT's LinkedIn citations fell ~36% in mid-August 2026, and its citations of Pulse articles fell ~86% from May to Oct while citations of feed posts rose. Perplexity and Copilot moved the other way. See linkedin-ai-citations.md.
What this means for strategy:
- Never concentrate AI-visibility work in one third-party surface. The Presence pillar's list (Wikipedia, Reddit, YouTube, podcasts, review sites, Quora) is a portfolio, not a menu to pick one from. A surface that's 2% of citations today can be 0% after one retrieval update — or vice versa.
- Owned-site fundamentals hedge the volatility. Platform deals and retrieval changes reshuffle third-party sources; your own agent-readable site is the one surface no platform can drop you from — and on Gemini it's already the dominant citation class.
- Treat any citation-share statistic as dated. The "Reddit = 1.8% of ChatGPT citations" class of stats (including the ones in this skill) are snapshots — check the date, and verify against your own citation monitoring (the DIY monitoring loop in SKILL.md) before betting budget on them.
- Speed is real: fresh content on retrieved surfaces can be cited within ~24 hours. AI search rewards freshness faster than classic SEO ever did.
Agent-readiness check taxonomy distilled from Vercel/Ora's Is Agentic (is-agentic.com) and Frase's Agent Readiness Checker (both August 2026, credited); citation-volatility events practitioner-reported (Ashni of Hype Partners (@ashnichrist) and others, August 2026) — labeled accordingly, verify against your own monitoring.