web-search-and-content

Strip HTML tags from caller-held markup. Call when you need plain text from agent-held HTML snippets without a browser. Returns tag-stripped text and length for $0.001 USDC via x402 on Base. No network, no keys, no retention; first-party local JSON only. Results are advisory; the caller owns budgets, auth, and production controls.

Extracts content from web pages by parsing and retrieving data from specified URLs.

You.com Search API: real-time web search returning ranked results grouped by section (results.web, results.news), each with url, title and description; web results also carry snippets. Use when an agent needs fresh, citable web data (news, markets, company facts) to ground a decision. Query params: query (required), count 1-100 (default 10), livecrawl (web|news|all; fetches live page contents, charged per result), country, freshness. Paid per call in USDC.

Extract forms, methods, actions, and fields for browser automation and workflow planning.

Generate llms.txt to improve AI search visibility — structured file for ChatGPT, Perplexity & Claude crawlers. Helps AI engines understand and cite your business. 0.50 USDC.

AI search visibility progress report — Four Layers scoring (SXO/AIO/GEO/AEO), what's working, gaps, and your next 3 highest-impact actions. 0.75 USDC.

Give a URL and a question; get an answer grounded in that page, WITH the passages the answer was drawn from. We fetch the page, chunk it, embed the chunks and your question, retrieve the closest 4 passages and answer from those only. Nothing is stored — it is a one-shot pipeline, not an index. The retrieved passages come back with the answer so you can check it rather than trust it, and when nothing on the page is relevant the product says so instead of letting the model improvise. Reaches the public internet only, with the same SSRF protections as the fetch product.

Emit the actual files that fix a site's AI legibility, ready to deploy: a real /llms.txt, a /robots.txt that stops excluding AI crawlers (with a unified diff against the current one), and a JSON-LD block for the page . Nothing is invented: every link in the llms.txt is discovered on the site and then FETCHED to confirm it answers 200, with failures dropped and counted, and every JSON-LD field is copied from something the page actually declares — anything that cannot be grounded is omitted rather than guessed. A small model writes only the prose, from the titles and descriptions we extracted; any URL it returns that is not in the verified set is discarded. Pairs with /x402/geo/audit: audit tells you what is wrong, this hands you the files. Re-run the audit afterwards to confirm the score moved.

Audit whether a website can actually be read, quoted and cited by AI agents, and get a prioritised fix list. Probes the homepage as each real AI crawler (GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, Claude-User, PerplexityBot, CCBot, meta-externalagent, Amazonbot) to catch the 429s, 403s and soft-404s that make a live site invisible; parses robots.txt per agent including the robots-only training tokens Google-Extended and Applebot-Extended; and checks llms.txt, structured data, machine-readable endpoints (OpenAPI, MCP, x402, well-known), sitemap, canonical and title/meta, plus how much of the page survives with JavaScript off. Fully deterministic — no model is called, so the same site scores the same twice. It does NOT ask ChatGPT or Perplexity whether they have heard of your brand; it measures the inputs that decide whether they can.

Scrape a target URL. Returns the raw page content.

Wikipedia summaries combined with Wikidata entity data in single structured JSON calls

Web scraping API with geo-targeting by country, state, and city. Fetch any public URL with JavaScript rendering support.

CAPTCHA solving API — reCAPTCHA, Turnstile, hCaptcha, image captchas, and more.

Scholarly works, authors, and citation data with h-index and topic information

Edges provides LinkedIn automation actions for data extraction, search, and discovery. Extract profiles, companies, posts, jobs, events, and more. Search across LinkedIn and Sales Navigator.

Comprehensive research search across arXiv, PubMed, OpenAlex, and Crossref in one call

Web scraping, crawling, and structured data extraction for LLMs.

POST a URL, receive cleaned Markdown plus title, byline, excerpt, site name, and word count. Powered by Mozilla Readability via SmartReader. Designed for agents that ingest articles into context every task.

arXiv academic paper search with Atom XML transformed to structured JSON for agents

Healthcare text de-identification engine (demo endpoint — SYNTHETIC/TEST DATA ONLY, never send real PHI; nothing is stored). Strips names, DOBs, member/claim IDs, phones, emails, addresses, SSNs; dates become a derived interval timeline (service-to-denial days, days-ago) so denial and timely-filing math survives de-identification. Returns scrubbed text + token map for local re-identification. Production use requires the licensed in-environment engine: contact laureennicholson@getautomatedrcm.com

One page load answers everything an agent needs to reason about a page: the DOM after JavaScript capped at 2 MB, the CDP accessibility tree flattened with parent references, every link with rel and target and an internal or external verdict, every form field with its label and autocomplete hint, console errors and warnings with source and line, a screenshot, navigation timings and a summary of the requests the page fired. Chrome runs without credentials, without cookies and without login, so a s

On-demand web research digest — free-source curated (Wikipedia + Hacker News + Jina Reader). GET /research?q=<topic>; pay per request in USDC on Base. Runs on any install, $0 source cost.

Decision procedure for an agent deciding whether to pay for structured NLP extraction (diffbot-style entity, sentiment, and relation extraction) versus free regex or heuristic parsing versus inline LLM extraction. Ordered workflow: score the document batch by structure need tier (0 plain text search is enough, 1 named entities needed, 2 typed relations and entity graphs needed, 3 cross-document entity resolution needed), estimate per-document cost on each rail in USDC, compare against an inline LLM pass on a sample, run schema quality gates on the sample output, then commit to the cheapest rail that meets the tier. Includes kill criteria and falsifier rules. Blunt thresholds, no marketing.

Extract keywords and key phrases from text. Returns ranked keywords with relevance scores. 30 sats.