SEO Grade Calculator
Score your site's SEO performance
Join our community of websites already using SEOJuice to automate the boring SEO work.
See what our customers say and learn about sustainable SEO that drives long-term growth.
Explore the blog →Run a full scan for discovery, crawl control, markdown, MCP, OAuth, and agent-facing standards. Then keep the score, the evidence, and the before-versus-after history.
What it means
A site that scores fine on a traditional SEO audit can still be invisible to AI agents. Different reading patterns expose different gaps.
A search engine crawler and an AI agent read your site for different reasons. Googlebot wants to rank a page in a results list. ChatGPT browse, Perplexity, Claude's web tools, and Gemini want to answer a question — often by quoting you, citing you, or completing a task on a user's behalf. Those reading patterns expose different gaps.
This tool checks the four things that determine whether an AI agent can find, read, trust, and act on your site.
Discoverability covers robots.txt, sitemap.xml, and the /.well-known/ entries agents now look for first.
AI readability is whether your HTML is clean enough for a language model to parse without JavaScript, whether you publish
a markdown alternate, and whether your structured data maps to your visible content. Policy and identity tells agents
which bots you welcome and how your content can be used. Action surfaces is whether agents can do anything beyond
reading: an MCP server, an API catalog, schema.org actions, an OAuth-protected programmatic surface.
We grade each category independently and combine them into a 0–100 readiness score, then map that to a 0–5 readiness level so the next move is clear. Most sites land at Level 1 or Level 2 on first scan, regardless of brand size — agent-readiness is genuinely new ground.
How it works
Each weighted by how much it affects whether AI agents can discover, read, and act on your site.
Checks whether agents can find your site-level instructions and machine-readable entry points quickly.
Looks at whether agents can consume your pages in a cleaner representation than raw rendered HTML.
Measures how clearly you express AI crawler policy and downstream usage preferences.
Measures whether an agent can discover actual machine interfaces instead of stopping at static content.
Who uses this
Run a public scan before a sales call, screenshot the score, drop the share URL into the proposal. Treats agent-readiness as a measurable retainer line item.
Use the score history to prove that bot-infrastructure work moved the needle. Re-scan after each shipped fix and watch the level climb.
Get a one-page snapshot of where AI agents currently rank you, with a prioritized fix list and an honest assessment of how big each fix is.
Reading your report
Overall score & level
0–100 score with a 0–5 level mapping. Level is the number you reference when discussing progress.
Category breakdown
Per-category bars. A red category is where the next sprint should focus, regardless of overall score.
Top recommended fixes
Click-to-expand. Each fix shows what's broken, why it matters, and developer-ready instructions. One-button "Copy developer brief".
Full audit evidence
Every check we ran with the actual HTTP responses, headers, and snippets. Open this when you want to verify a finding.
Score history
Once you have two scans, this becomes a trend chart. Use it to prove an infra change moved the score.
Benchmark
Where the scanned domain sits relative to other completed scans for the same site type.
What good looks like
Effectively invisible to AI agents
Missing robots.txt or sitemap, no structured data, JavaScript-rendered content, no /.well-known/ entries. ChatGPT browse and Perplexity will skip the site or use stale Google cache.
Readable, but not yet identified
Agents can fetch and parse the site cleanly. What's missing is bot-specific identity (LLM policy, content-use signals) and discovery surfaces (API catalog, MCP). Most sites land here.
Agent-transactable
Discovery, auth, and action surfaces are live. Agents can identify themselves, read your policy, and call your APIs on a user's behalf. A small percentage of sites are here today.
Each check returns one of four states: pass (full credit), fail (zero credit), warn (partial credit, usually because we found a signal but it was incomplete), or neutral (informational, not scored). Within a category, the category score is the weighted average of its individual checks; an optional check we couldn't reach is dropped from the denominator rather than counted as a failure.
The four category scores combine into the overall 0–100 score using the weights shown in the table above. We deliberately avoid a single-pass / single-fail gate: a low Discoverability score caps everything that depends on it, but a strong Action Surfaces score still pulls the overall up because it represents real work that helps real agents.
Level mapping: 0 (0–9, invisible), 1 (10–24, indexed only), 2 (25–49, readable), 3 (50–69, identified), 4 (70–84, transactable), 5 (85+, agent-native).
We re-fetch the site fresh for each scan, including robots.txt, sitemap, and a sample of pages from the sitemap. We do not
rely on cached or third-party data. The sample is small (typically 1–3 pages) so the scan is fast and the cost is bounded.
FAQ
It means an AI system can discover your rules, read your content in a clean format, and find the machine-facing interfaces behind the site without reverse-engineering the frontend.
robots.txt is only one part of the picture. A site can be crawlable and still be hard for agents to use if it lacks markdown delivery, discovery headers, API metadata, OAuth discovery, or MCP-style machine entry points.
More discovery starts with AI systems and conversational interfaces. If your site is hard for machines to read or act on, you are less likely to be cited, routed to, or used in agent-led buying flows.
Reports are public at /ar/<domain>. A rerun does not overwrite the last completed scan. The public page keeps the latest finished report visible while a new run is still in progress, and same-day reruns stay distinct in history.
No, it complements it. Traditional SEO audits check whether Googlebot can crawl and rank your pages. Agent-readiness checks whether the new generation of AI agents can discover, read, and act on your site. A site can pass one and fail the other; in practice you want both.
llms.txt is a single-file proposal for surfacing AI-readable content. It's one signal we check (under AI Readability), but agent-readiness covers a much wider surface — robots policy, structured data, action interfaces, identity. A perfect llms.txt alone gets partial credit in one category.
No. MCP is one of several action-surface signals and only matters for sites that want agents to take actions (book, buy, query). A documentation site, blog, or marketing site can score 70+ without an MCP server.
After every shipped change to robots.txt, sitemap, structured data, or any of the discovery surfaces. The score history will show whether the change actually moved the needle. For passive monitoring, monthly is fine.
Yes. Email vadim@seojuice.io with the domain and we'll delete the public report within one business day.
A few causes: a third-party service we depend on returned an error, the sample of pages we scanned was different, your CDN served slightly different bytes, or a check we recently calibrated weighed signals differently. If you see a swing larger than ~5 points without a code change, compare the audit evidence — the diff is usually visible there.
Site type adjusts the benchmark we compare your score against and which optional checks are weighted higher. An ecommerce site benefits more from product schema and a robust API catalog; a content site benefits more from clean markdown alternates.
The report URL is publicly accessible (it has to be, for the share/benchmark feature to work). The raw HTTP fetches we ran during the scan are stored only as much as the report needs to show the evidence — we don't retain page bodies beyond the previews shown in the audit.
Link: rel="alternate" type="text/markdown".
robots.txt + AI-specific extensions describing which automated agents you allow and how content may be used.