Static analysis tool that scores whether a website is machine-readable by AI agents: crawlable, parseable pricing and contact info, structured data, and API discoverability.
AI agents increasingly act as intermediaries in B2B research and procurement. A site built only for human browsers (pricing rendered client-side, no structured data, crawlers blocked in robots.txt) is invisible to those agents regardless of how it looks to a person.
Existing GEO tools (Profound, Peec AI) measure whether a brand is mentioned in AI answers. They do not check whether an agent can extract pricing, contact details, or a machine-readable product description from the site itself. agentready checks that directly, with deterministic rules rather than an LLM call.
8 checks run against the static HTML response of the target URL. No LLM involved — every result is reproducible.
| Check | Points | Tests |
|---|---|---|
| AI crawler access | 20 | robots.txt does not block GPTBot, ClaudeBot, PerplexityBot |
| llms.txt | 15 | Present, with H1 title, blockquote summary, linked pages |
| Structured data | 20 | JSON-LD present; FAQPage/Product/SoftwareApplication scores above a generic Organization type |
| JavaScript rendering | 15 | Key content present in the raw HTML response, since AI crawlers do not execute JavaScript |
| Pricing parsability | 15 | Prices visible in static HTML |
| Contact parsability | 10 | Email or phone findable without JavaScript execution |
| API discoverability | 10 | /openapi.json or /.well-known/ai-plugin.json present |
| Sitemap | 10 | Valid sitemap.xml |
Score is normalized to 0–100.
Backend (FastAPI):
git clone https://github.com/mohanish3/agentready
cd agentready/backend
pip install -r requirements.txt
uvicorn main:app --reload --port 8000Frontend (Next.js), in a second terminal:
cd agentready/frontend
npm install
npm run devOpen http://localhost:3000 and enter a URL to scan.
Fetches the target URL and its robots.txt with a declared scanner user agent, runs the 8 checks against the static HTML response, and returns a per-check score breakdown with remediation notes.
- B2B growth and RevOps teams auditing sites ahead of agentic-commerce workflows
- Developers who want a technical checklist rather than a brand-mention tracker
- Agencies auditing client sites before an AI-driven GTM push
MIT