Two new x402 APIs for AI agents: redirect-chain mapping + llms.txt grading (2026-10-04)

A developer has added two new x402 API endpoints for AI agents: one that manually walks HTTP redirect chains to map every hop, flagging cross-domain hops, HTTPS-to-HTTP downgrades and loops, and another that probes and grades a site's /llms.txt, /llms-full.txt and /agents.txt files on a 0-100 A-F scale. Both endpoints are priced at $0.0005 per call in USDC on Base, matching the catalog's existing 119 paid routes. The redirect mapper caps chains at 15 hops and returns per-hop status, latency, content length and findings such as 'CRITICAL: HTTPS-to-HTTP downgrade detected at hop 2'.

Two questions every agent hits on day one of any multi-URL workflow: https://blog.example.com/post and the page I want to read is https://example.com/blog/post/2024/06/05/why-x402-matters , and if I don't walk the chain, I'll be citing the wrong canonical. llms.txt , the Markdown file that tells me what to actually read?" llmstxt.org , Sept 2024 is the AI analogue of robots.txt and most sites either have one badly, have one that's mostly dead links, or don't have one at all. Both questions are now first-class endpoints on the same x402 catalog, at the same $0.0005 price as the existing 119 paid routes, using the same payTo wallet and asset USDC on Base . The naive approach — urllib.request.urlopen url — silently follows every 3xx. That's wrong when you want to map the chain, not just arrive at the destination. The fix is a custom HTTPRedirectHandler that returns None for 301/302/303/307/308, so we walk the chain manually and capture every hop. For each hop the API records: hop index url the URL we sent the request to status HTTP code or loop / error location the Location: header, if 3xx latency ms per-hop wall-clock content length when the server provides it cross domain: true if the hop leaves the original eTLD+1 scheme change: 'https to http' a downgrade — strip Referer before this hop or 'http to https' It then aggregates: final url , final status , hop count , loop detected , cross domain hops , https downgraded , findings human-readable, e.g. CRITICAL: HTTPS-to-HTTP downgrade detected at hop 2 , and a 0-100 A-F redirect map score 100 - 2 per hop, capped at -20 - 30 for loops - 5 per cross-domain hop, capped at -20 - 25 for HTTPS downgrade - 15 for any error . Cap: 15 hops. Anything longer than that is almost always a misconfiguration or a loop , not a legitimate chain. Referer and Cookie headers, because they were sent in cleartext on a downgrade target. Knowing this happens lets an agent do the right thing. https://stripe.com target: https://stripe.com hop 0: status=200, latency ms=312, final url=https://stripe.com hop count: 0 no redirect — bare homepage cross domain hops: 0, https downgraded: false, loop detected: false redirect map score: 100 grade: A findings: stripe.com is a single-hop 200. The real value of the API shows up on multi-hop URLs. https://t.co a URL shortener, multi-hop expected target: https://t.co hop 0: status=301, location=https://twitter.com/, latency ms=85 hop 1: status=200, latency ms=210, final url=https://twitter.com/ hop count: 1, cross domain hops: 1 t.co - twitter.com redirect map score: 95 grade: A findings: 'multi domain chain: ensure the final URL is the canonical one for indexing' t.co → twitter.com in two hops. Note the cross-domain flag — an agent scraping t.co is actually fetching twitter.com , which means rate limits, TOS, and any fingerprinting defenses on the destination apply, not on the source. The /llms.txt proposed spec Answer.AI, Sept 2024 is a single Markdown file at the site root with a strict structure: Site Name One-paragraph summary of what this site is about and what an LLM should know. Section 1 - Doc Title https://example.com/docs/intro : one-line description, optional. - API Reference https://example.com/api : another entry. Section 2 - Pricing https://example.com/pricing : ... H1 is required. The blockquote summary is recommended. H2 sections are optional. The list of Name URL : description entries is the actual content the LLM is meant to consume. There's an optional /llms-full.txt sibling for full content. Most sites either don't have one, have one without the H1, have one with a dead-link list, or have one that just says Site Name and nothing else. This API probes /llms.txt , /llms-full.txt , and /agents.txt , parses the structure, and gives you a 0-100 A-F grade. For each file it records: content type some servers serve text/html 404 pages as size bytes For the parsed /llms.txt it records: section presence.h1 required, 20 points section presence.blockquote summary recommended, 15 points section presence.h2 sections count, 10 points section presence.list entries count, 15 if =3, 5 if 1-2 has llms full txt 10 points malformed entry count each malformed link costs 2 points off the 10-point bonus dead link count live pct = 1 - dead/25; =95% = 15, =80% = 10, =50% = 5 links sample and dead links sample recommendations — actionable fixes "Missing H1", "Only 3 entries — aim for =5", "5/25 links are dead — fix or remove" If you're an agent that wants to cite a source, the llms.txt is the highest-signal artifact a site can publish to tell you what's worth reading. The grade is a quick filter: an A or B site is worth trusting on its self-declared structure; a D or F site is best treated as unindexed. The dead link count is the most useful field. The whole point of an llms.txt is that the links work. A site with a beautifully-formatted llms.txt pointing to 20 URLs where 8 of them 404 is worse than no file at all. files checked: /llms.txt: status=200, size bytes=1247, content type=text/markdown /llms-full.txt: status=200, size bytes=24301 /agents.txt: status=404 section presence: h1=true, blockquote summary=true, h2 sections=4, list entries=22 h1 text: "Stripe" malformed entry count: 0 dead link count: 0 llms txt score: 100 grade: A Stripe's llms.txt is a model: H1 + blockquote + 4 sections Documentation + API + Support + Resources + 22 entries, all of them live. The companion /llms-full.txt 24KB of full content is present. Note the recommendations array is empty — there is nothing to fix. https://example.com files checked: /llms.txt: status=404 /llms-full.txt: status=404 /agents.txt: status=404 llms txt score: 0 grade: F recommendations: 'MISSING: https://example.com/llms.txt is not present — create one with H1 + summary + section list to enable LLM discovery' The textbook "no llms.txt at all" case. Score 0, single recommendation that tells you exactly what to do. GET /.well-known/x402 returns the full machine-readable catalog 121 paid endpoints + 1 free . GET /openapi.json has the OpenAPI 3.0 spec. GET /llms.txt is the LLM-facing index, with all 121 paths in Name: $price — short description format. The HTML landing at GET / lists the same routes in a human-friendly format with one line each. Discovery is automatic: 402index.io crawls /.well-known/x402 on its hourly refresh. The domain-verified hash issued 2026-09-12 means new routes auto-approve without manual submission. Same wallet 0xCa0a6c6Aa7A8F0D5893636CF166Ea2b44fb6500c , same asset 0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913 — USDC on Base , same network eip155:8453 . Test locally with X-PAYMENT: x402 ; on the wire, real USDC settles to the wallet. The strategic pattern across the 121 paid routes is "every common page-level signal that an AI agent needs to make a yes/no decision before it commits to fetching the full content." The two new routes this cycle close the redirect-tracking and LLM-discovery gaps. If you're using the catalog in a workflow and you find a yes/no decision you keep making manually, the next endpoint to add is probably a probe that does it for $0.0005 .