Vercel Introduces ‘Is Agentic’, a Free Agent-Readiness Scoring Tool That Audits Public Websites Using Ora’s 100+ Checks

Vercel has launched Is Agentic, a free tool that scores how readily AI agents can discover, access, understand, and use a public website, using Ora's 118 checks across four layers: discovery (20 points, 15 checks), access (30 points, 41 checks), usability (40 points, 56 checks), and payments (10 points, 6 checks). The tool is available at no cost with no API key or billing account required, and it provides a letter grade from A+ to F, with Vercel regrouping checks into its own scoring model that excludes not-applicable checks and averages duplicated check IDs across MCP surfaces.

Vercel has released Is Agentic https://is-agentic.com/ , a public tool that scores how readily AI agents https://x.com/vercel dev/status/2090857388924661915 can discover, access, understand, and use a website. Scans are run and scored by Ora https://ora.ai/ , an agent-experience research company from era labs. Vercel operates the interface, report pages, storage, and the grouping that produces the displayed score. Is it deployable? Yes — at zero cost. Is Agentic is currently free https://is-agentic.com/pricing.md , with no paid plans, subscription charges, or per-report fees. The public site, read-only API, CLI, and MCP server require no API key or billing account. Enter a URL in the browser, or run the CLI: npx is-agentic <domain npx is-agentic <domain --json Which level of company : Any organization owning a public web surface. Seed-stage startups get a free baseline audit with no procurement. Mid-market SaaS teams can wire --json output into CI as a regression gate. Enterprises can benchmark documentation portals, developer sites, and commerce surfaces across business units. Industries : Developer tools and SaaS, e-commerce and retail, travel and hospitality, fintech, marketplaces, healthcare provider directories, and media or documentation publishers. Anywhere an agent may shop, book, integrate, or cite on a user’s behalf. Applications : Pre-launch agent-readiness audits. CI checks that fail builds when server-rendered content regresses. Competitive benchmarking against public scores. Validating an MCP server or OpenAPI surface is actually discoverable. Prioritizing technical fixes with copy-pasteable remediation prompts. What Exactly it Measures Ora’s methodology https://ora.ai/methodology walks four layers an agent moves through. Discovery carries 20 points across 15 checks. Access carries 30 points across 41 checks. Usability carries 40 points across 56 checks. Payments carries 10 points across 6 checks. That totals 118 checks, matching Vercel’s “100+ checks” claim. Ora says the checklist is reverse-engineered from real agent runs, not authored by opinion. Ora also publishes a letter scale: A+ at 95–100, A at 86–94, B at 70–85, C at 48–69, D at 28–47, and F at 0–27. Vercel regroups those checks into its own displayed scoring model https://is-agentic.com/methodology . Essential checks share an 80-point pool. Recommended checks share a 20-point pool. Emerging signals add a bonus capped at five points, and their absence never lowers a score. Not-applicable checks are excluded rather than counted as failures. Partial results receive proportional credit, and duplicated check IDs across MCP surfaces are averaged. That applicability logic matters. Recommended checks activate only when scan evidence positively identifies an API, OAuth flow, GraphQL endpoint, MCP server, developer portal, or commerce surface. A marketing site is not penalized for omitting an interface it never claimed to offer. Inside a report Every finding ships with observed evidence and, where available, a concrete recommendation. A live API response shows the shape: score , score label , scanned at , eligible checks , a score breakdown with earned and available points per tier, and an issues array. Each issue carries id , name , details , recommendation , result , and tier . Real check IDs include content-no-js , agent-friendly-404 , markdown-negotiation-vary , json-ld , sitemap , trust-anchors , and metadata-completeness . The 404 check asks for a real HTTP 404 status rather than a 200 serving an app shell. Reports also include an observed agent journey showing where one agent hit friction. Vercel keeps that run outside the numeric score, since one task cannot represent every agent. “Prompt to fix” converts actionable recommendations into an implementation brief for a coding agent. Machine interfaces Three read-only surfaces expose completed reports. The report API https://is-agentic.com/docs lives at /api/v1/report , rate-limited to 120 requests per client IP per 60-second window. Errors use RFC 9457 problem details with stable codes such as invalid url , report not found , and rate limit exceeded . The OpenAPI description https://is-agentic.com/openapi.json is the supported integration contract, discoverable through an RFC 9727 API catalog. Deprecations signal via RFC 9745 headers, with Sunset announced at least 90 days ahead. The MCP server at https://is-agentic.com/mcp exposes is agentic get report , is agentic get methodology , and is agentic get developer docs over Streamable HTTP. Hosts supporting MCP Apps render an interactive score card. An official agent skill installs via npx skills add vercel-labs/is-agentic . The site practices what it scores. Report pages render the score in the initial HTML response, serve a Markdown variant under Accept: text/markdown , and set Vary: Accept so shared caches never mix representations. Key Takeaways - Is Agentic scores public sites on agent discovery, access, usability, and payments — free, with no API key. - Ora supplies 118 checks across four weighted layers; Vercel regroups them into Essential 80, Recommended 20, Bonus 5. - Not-applicable checks are excluded, so marketing sites are not penalized for missing APIs or commerce surfaces. - Reports ship as HTML, Markdown, JSON, CLI output, and MCP tools, all read-only and unauthenticated. - Vercel states it is not a certification, security audit, or accessibility review — treat it as a prioritized technical review. Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.