jevseo.vercel.app
Finalizing the report
Preparing the stored Is Agentic score
Essential—
Recommended—
Bonus signals—
jevseo.vercel.app
Preparing the stored Is Agentic score
jevseo.vercel.app
These checks describe whether an ordinary agent can enter, read, and operate the public site.
Agents can reach the site
Crawler access and bot defenses.
Core content is available
Useful content remains accessible without a fragile browser-only path.
Navigation fails safely
Redirects and missing pages give agents a recoverable path.
Controls are understandable
Forms and interactive controls expose usable names and structure.
The public website is always evaluated. Optional surfaces appear when the scan finds positive evidence that they apply.
Ready with gaps
Weighted across 16 applicable checks
Ready with gaps
Weighted across 1 applicable check
Critical access gaps come first, followed by other applicable readiness gaps.
Keep the correct HTTP 404 status. The remaining requirement is a Markdown error body when agents request Accept: text/markdown. Include at least 20 characters explaining the error and a link to your docs, sitemap, or llms.txt. Verify with curl -sS -L -i -H 'Accept: text/markdown' https://yourdomain.com/some-path-that-does-not-exist. Check both the final 404 status and the Markdown body with Content-Type: text/markdown. Checking the status alone does not verify the missing requirement.
Enable Markdown negotiation on the scanned homepage. Supporting it only on /docs or a separate .md URL does not satisfy this check. Requests with Accept: text/markdown must receive a nonempty Markdown body with Content-Type: text/markdown and Vary: Accept. Keep serving HTML for Accept: text/html. Adding Vary alone does not create a Markdown response. Verify both with curl -sS -L -i -H 'Accept: text/markdown' https://yourdomain.com/ and curl -sS -L -i -H 'Accept: text/html' https://yourdomain.com/. Check the final response headers and body: Markdown with Vary: Accept for the first request, HTML for the second.
Add Organization JSON-LD that includes both contactPoint (with email/phone and contactType) and address (PostalAddress). This lets AI verify your business legitimacy and answer contact queries.
Checks are grouped by relevance for Docs & content sites.
8294 chars, semantic headings (1 H1 + 8 H2s + 11 H3s), 33.9% content ratio
Site accessible to 6 AI agent user-agents
No meta-refresh stubs, JavaScript-redirect stubs, or cross-domain hops across 6 checked pages
All 5 sampled pages are publicly readable (5 with substantive content)
Homepage https://jevseo.vercel.app does not meet the Markdown negotiation requirements: Accept: text/markdown returned text/html; charset=utf-8; Vary header missing Accept (got "none")
How to pass
Enable Markdown negotiation on the scanned homepage. Supporting it only on /docs or a separate .md URL does not satisfy this check. Requests with Accept: text/markdown must receive a nonempty Markdown body with Content-Type: text/markdown and Vary: Accept. Keep serving HTML for Accept: text/html. Adding Vary alone does not create a Markdown response. Verify both with curl -sS -L -i -H 'Accept: text/markdown' https://yourdomain.com/ and curl -sS -L -i -H 'Accept: text/html' https://yourdomain.com/. Check the final response headers and body: Markdown with Vary: Accept for the first request, HTML for the second.
The nonexistent path https://jevseo.vercel.app/__ora-404-probe-hz3a6awc correctly returns HTTP 404. Partial credit: no Markdown error body was detected.
How to pass
Keep the correct HTTP 404 status. The remaining requirement is a Markdown error body when agents request Accept: text/markdown. Include at least 20 characters explaining the error and a link to your docs, sitemap, or llms.txt. Verify with curl -sS -L -i -H 'Accept: text/markdown' https://yourdomain.com/some-path-that-does-not-exist. Check both the final 404 status and the Markdown body with Content-Type: text/markdown. Checking the status alone does not verify the missing requirement.
Valid sitemap found at https://jevseo.vercel.app/sitemap.xml with 9 entries
Rich JSON-LD identity: SoftwareApplication with name, description, url, and category/offers (3 blocks)
All metadata signals present: canonical URL, lang="en", og:image, og:type
Organization schema found but missing: contactPoint, address
How to pass
Add Organization JSON-LD that includes both contactPoint (with email/phone and contactType) and address (PostalAddress). This lets AI verify your business legitimacy and answer contact queries.
All trust anchor pages verified: About, Contact, Privacy
All 6 measured pages fit an agent context budget (largest ~2K tokens)
Agent struggled to find developer resources by name - only 1 result(s) out of 10. Not found in this search sample: API docs, OpenAPI spec, auth docs, SDK documentation
How to pass
Check whether your developer resources (API docs, OpenAPI spec, auth docs, developer portal, MCP server, SDK documentation) surface in name-based searches. If they do not, use predictable URLs, link them in llms.txt, and include your product name in page titles and headings. This result reflects one search sample.
NPM package found: vercel - "The command-line interface for Vercel"
Agent config found: github.com/akashpriyadarshii/jev-curate/blob/main/AGENTS.md
Found in ChatGPT app directory: "Vercel"
100% of 9 sampled sitemap entries carry lastmod; newest is 2 day(s) old
Found the llms.txt at https://jevseo.vercel.app/llms.txt.
The llms.txt starts with a heading and has 18 lines, but it contains no markdown links.
How to pass
Format your llms.txt as a navigation index: start with a markdown heading, include markdown links to deeper resources, and keep it under 30,000 characters. If you have more to say, move long-form content into /llms-full.txt or per-section files (e.g. /docs/llms.txt, /api/llms.txt) and link to them from the main index.
Pricing structured data (schema.org/Offer) found
When-to-use guidance found in llms.txt
Some extended schema types found: FAQPage - add FAQPage, Service, or AggregateRating for full coverage
How to pass
Expand your JSON-LD beyond Organization/WebSite. Add FAQPage for common questions, Service or Product for offerings, AggregateRating or Review for social proof, and BreadcrumbList for navigation context.
Code fences balanced across 1 markdown document
Onboarding signals described but not verified live: free tier available, self-serve key generation
How to pass
Offer a free tier or trial, self-serve API key generation, and a sandbox environment. Agents can't fill out 'contact sales' forms.
First-party MCP server published by product org (npm, @vercel/mcp-adapter, 41561 score). Add live handshake at /.well-known/mcp for full credit.
How to pass
Build an MCP (Model Context Protocol) server exposing your API as tools. Use Streamable HTTP transport for full score. This lets Claude, ChatGPT, and other AI agents call your product natively.
CLI tool found on PyPI: vercel-cli
SDK package found only in npm
How to pass
Publish official SDK packages across multiple language ecosystems (npm, PyPI, Go modules, RubyGems). Auto-generate them from your OpenAPI spec using tools like openapi-generator. For each package set the project URL or homepage to your product domain (package.json repository/homepage, PyPI Home-Page or project_urls, RubyGems homepage_uri) - this is how agents verify the package is your official SDK.
Server HTML is a well-structured document (main=true, landmarks=4/4, h1=1, maxHeadingSkip=1).
37 native controls, 0 non-native div-soup affordances (100% native).
37/37 interactive elements have a computable accessible name (100%).
No hidden instruction text detected in accessibility-tree attributes or off-screen content.
What does jevseo.vercel.app do and who is it for? Explain it back to me.
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Source: Ora API
What does jevseo.vercel.app do and who is it for? Explain it back to me.
7 steps4 reasoning steps
The agent successfully gathered comprehensive information about jev-seo's purpose, audience, pricing, and positioning from the homepage, but the site proved navigationally limited—all substantive content came from a single repeated fetch that returned the same overview, forcing the agent to identify gaps and acknowledge unknowns (data freshness, scale tradeoffs, real-world use cases) that the site did not explicitly address.