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metaend Grade — Agent Readiness Checker
See your site the way an AI agent sees it
AI agents read your site differently than people do, and most sites fail them. metaend Grade scans any URL and grades its agent readiness A to F: discovery files, content negotiation, A2A, ERC-8004, payments. Each fail comes with a fix. Free to scan.
Hey Product Hunt 👋
For thirty years the web had one audience: people. That changed. AI agents (Claude, ChatGPT, Perplexity, and the autonomous ones that crawl, retrieve, and transact) now read the web too, and they read it completely differently. They do not look at your hero image. They look for machine-readable signals: llms.txt, content negotiation, structured data, an agent card, a way to pay. Most sites expose none of it, so the agent shrugs and moves on. You never even see the visit.
I kept hitting this while building agent tooling, so I built something to measure it.
metaend Grade scans any URL the way an agent would and hands back a grade from A to F across ~20 scored checks plus informational ones:
📄 Discovery files (llms.txt, robots, sitemap)
🔀 Content negotiation (does your URL return JSON/Markdown when asked?)
🤝 A2A agent cards + ERC-8004 trustless-agent signals
🪪 Identity (DID, WebFinger, Nostr)
💸 Machine-payment support
Every failed check comes with a plain-language fix. It is a to-do list, not a vanity score.
Two things I am proud of:
It dogfoods itself. The scanner serves its own llms.txt, agents.txt, agent card, security.txt, and JSON-LD, and content-negotiates its own root. If you grade the web on agent-readiness, the grader had better pass its own test.
It is built for agents to use, not just humans. GET /api/v1/scan?url=… returns a compact graded report as JSON. One agent can scan a site and act on the result in a single move.
Scanning is free up to a daily allowance. Beyond that, scans are paid over the open x402 standard: real EIP-3009 USDC payments settled on-chain on Base and Arbitrum, with replay protection and receipts. Humans can top up with the same rail to skip the per-site cooldown. No closed-vendor lock-in, by design.
Stack for the curious: Bun, Astro SSR with a React island, TypeScript strict with Zod, bun:sqlite, SSRF-hardened, on Fly.
Go scan your site and drop your grade in the comments. I will be here all day answering everything. Bonus points if you scan a site you think is "well built" and watch it get a D. 😅
About metaend Grade — Agent Readiness Checker on Product Hunt
“See your site the way an AI agent sees it”
metaend Grade — Agent Readiness Checker was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #154 on the daily leaderboard. AI agents read your site differently than people do, and most sites fail them. metaend Grade scans any URL and grades its agent readiness A to F: discovery files, content negotiation, A2A, ERC-8004, payments. Each fail comes with a fix. Free to scan.
On the analytics side, metaend Grade — Agent Readiness Checker competes within Artificial Intelligence — topics that collectively have 471k followers on Product Hunt. The dashboard above tracks how metaend Grade — Agent Readiness Checker performed against the three products that launched closest to it on the same day.
Who hunted metaend Grade — Agent Readiness Checker?
metaend Grade — Agent Readiness Checker was hunted by Chris. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of metaend Grade — Agent Readiness Checker including community comment highlights and product details, visit the product overview.
Chris