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Scrape a domain's homepage `<head>` for public brand assets — favicon, og:image, theme-color, og:site_name, JSON-LD `Organization.logo`. Use to enrich CRM records, build company-card UIs, or correlate a lead's site to their visual identity (no manual screenshot required). Strictly homepage-only (path `/`); we do NOT crawl. Ethical floor: target's robots.txt is honoured — `Disallow: /` for ContrastAPI OR `*` returns 403 `error.code = robots_txt_disallow` and we DO NOT fetch. `Cache-Control: no-store` / `private` from the target is respected (response is built but NOT written to our cache; `cache_respected=false` flags this). Per-target eTLD+1 throttle (60 req/min) prevents weaponising via subdomain rotation. All URL fields are absolute and `_untrusted` (DO NOT execute or shell-out — the target controls these strings). Free: 30/hr, Pro: 500/hr. Returns {domain, fetched_url, status_code, favicon_url_untrusted, og_image_url_untrusted, theme_color, site_name_untrusted, logo_url_untrusted, cache_respected, summary}. Returns 502 on DNS/TCP/TLS failure; 403 `robots_txt_disallow` when the target opted out.

Evaluate caller-provided candidate context and return decision-ready output. This is the primary FreshContext judgment path: it does not fetch, crawl, scrape, browse, read folders, or call adapters.

Scrape YC company listings from a ycombinator.com/companies search URL. Returns name, batch, status, tags, and description per company. Freshness is unknown — YC listings carry no reliable per-company update date.

Direct PromQL passthrough for advanced questions that don't map cleanly to `list_benchmarks` / `get_benchmark`, e.g. "what was Mobula's p50 head-lag yesterday at 14:00 UTC" or "plot bridge fees over the last hour". Prefer the higher-level tools first; reach for this when you need: • a custom time window (instant query at a specific point, or range) • a derived metric (rates, ratios, deltas) • a histogram bucket aggregation across chains/regions Allowed metric namespaces (one prefix per OCB bench family): head_lag_seconds (aggregator latency) bridge_quote_latency_ms*, bridge_cost*, bridge_fees*, bridge_fix_fee*, bridge_gas*, bridge_output*, bridge_estimated_time*, bridge_quote_success l1_finality_*, l2_block_time_* metadata_coverage_*, metadata_api_latency_*, network_coverage_*, networks_supported, wallet_labels_* perp_fees_*, perp_funding_*, perp_venue_*, perp_execution_*, perp_liq_*, perp_realized_vol_*, ocb_buyback_*, ocb_oracle_*, ocb_validator_*, ocb_chain_* gas_error_*, gas_predicted_*, gas_realized_*, gas_oracle_* peg_* (stablecoin peg, both variants) solana_landing_* (TX landing observational + active) rpc_latency_*, rpc_call_total, rpc_health, rpc_archive_depth_supported relay_*, per_swap_margin_usd (bridge revenue) Queries referencing other metrics (operational/internal ones like `up`, `scrape_*`, `process_*`, `go_*`, `wallet_balance_*` or any label- enumeration shape) are refused with `{error, reason}`. Example: instant p50 over 1h for Mobula head-lag on Base: query_prom({ query: "quantile_over_time(0.5, head_lag_seconds{aggregator=\"mobula\",chain=\"base\"}[1h]) * 1000" }) Example: 7-day sparkline of average bridge fees: query_prom({ query: "avg_over_time(bridge_fees_percent[1d])", windowSec: 604800, steps: 168 }) Returns: `{ query, value }` for instant queries, `{ query, windowSec, series }` for range.

Recent news headlines for a ticker via Yahoo Finance's news feed. Returns structured articles (title, publisher, url, published_at, summary) for you to read and synthesize. Not a scraper -- uses Yahoo's aggregated feed, so coverage is strongest for large-cap US and Indian names.

Get upcoming investor-relations events for a stock — earnings webcasts, conference appearances, presentations, and shareholder meetings — scraped from the company's IR website. Returns events scheduled from now onward, soonest first, optionally filtered by event type. Coverage is partial — an empty answer distinguishes a coverage gap from a genuinely empty calendar. Only future events are returned; for past events and their transcripts use ListInvestorEvents / GetInvestorEventTranscript.

Get recent investor-relations press releases for a stock, scraped from the company's IR website. Returns the most recent news items (headline, publish date, summary when the source provides one, and link) in reverse-chronological order. Use this to see a company's latest official announcements straight from its IR page, distinct from third-party news. Coverage is partial — only companies whose IR page has been discovered and content-scraped have items, so an empty answer may be a coverage gap rather than corporate silence; the response says which case applies.

Enrich an organization's profile by domain. Alternative to Apollo org-enrich at 5x lower cost. Returns structured JSON with firmographic data, socials, tech stack, and contact info scraped from the website. 1. name (string) -- company name 2. description (string) -- company description / tagline 3. socials (object) -- LinkedIn, Twitter, Facebook, GitHub, Instagram URLs 4. techStack (array) -- detected CMS, frameworks, analytics tools 5. contactEmail (string) -- primary contact email from mailto: links 6. phone (string) -- phone number from tel: links 7. address (string) -- physical address from schema.org structured data 8. industry (string) -- detected industry vertical 9. logo (string) -- logo URL if found Example output: {"name":"Stripe","description":"Financial infrastructure for the internet","socials":{"linkedin":"https://linkedin.com/company/stripe","twitter":"https://twitter.com/stripe"},"contactEmail":"info@stripe.com","techStack":["React","Next.js","Cloudflare"],"address":"354 Oyster Point Blvd, South San Francisco, CA"} Use this BEFORE sales outreach, CRM enrichment, competitive research, or account-based marketing. Essential for qualifying leads and enriching organization profiles from just a domain. Drop-in replacement for Apollo company enrichment. Do NOT use for tech stack only -- use website_detect_tech_stack instead. Do NOT use for SEO data -- use seo_audit_page instead. Do NOT use for person data -- use person_enrich_from_email instead. Do NOT use for domain WHOIS/DNS -- use domain_lookup_intelligence instead.

Enrich an organization's profile by domain. Alternative to Apollo org-enrich at 5x lower cost. Returns structured JSON with firmographic data, socials, tech stack, and contact info scraped from the website. 1. name (string) -- company name 2. description (string) -- company description / tagline 3. socials (object) -- LinkedIn, Twitter, Facebook, GitHub, Instagram URLs 4. techStack (array) -- detected CMS, frameworks, analytics tools 5. contactEmail (string) -- primary contact email from mailto: links 6. phone (string) -- phone number from tel: links 7. address (string) -- physical address from schema.org structured data 8. industry (string) -- detected industry vertical 9. logo (string) -- logo URL if found Example output: {"name":"Stripe","description":"Financial infrastructure for the internet","socials":{"linkedin":"https://linkedin.com/company/stripe","twitter":"https://twitter.com/stripe"},"contactEmail":"info@stripe.com","techStack":["React","Next.js","Cloudflare"],"address":"354 Oyster Point Blvd, South San Francisco, CA"} Use this BEFORE sales outreach, CRM enrichment, competitive research, or account-based marketing. Essential for qualifying leads and enriching organization profiles from just a domain. Drop-in replacement for Apollo company enrichment. Do NOT use for tech stack only -- use website_detect_tech_stack instead. Do NOT use for SEO data -- use seo_audit_page instead. Do NOT use for person data -- use person_enrich_from_email instead. Do NOT use for domain WHOIS/DNS -- use domain_lookup_intelligence instead.

Save a link or a text note to the user's MarkIt library. Pass a URL to save and scrape a web page, or plain text to save a note. Optionally attach a note - your own context, e.g. why it matters or how it relates to the conversation - and, for a text note, a title. MarkIt scrapes, auto-categorizes, and indexes the item in the background so it turns up in later searches. Each save counts as one capture against the monthly limit; saving a URL already in the library is a no-op.

List courts with optional filtering by jurisdiction type, active/inactive status, and scraper coverage. Primarily used to discover court IDs for use in search and filter parameters across all other courtlistener tools. Defaults to the active bench — the courts CourtListener still scrapes; pass status:'inactive' for historical courts or status:'any' for every court. A bundled snapshot returns the complete list of matching court IDs without paging whenever the filtered set fits the response budget, which covers the default bench and every jurisdiction filter. Full court records — names, citation strings, scraper status — come live from CourtListener at a fixed 20 rows per page, so pull those only when a court ID alone is not enough.

Poll an async tracking job by job_id. Returns status (queued, running, completed, or failed). On completion, product is populated; on scrape failure, error is populated (HTTP 200 job lookup — not a transport error). On failure, returns a structured error object with fields error.code, error.message, error.http_status, error.retry_recommended, and error.retry_after_seconds.

Scrape any website through Scrapingdog's rotating proxies and return its content. Returns HTML by default, or clean markdown with format:"markdown" (ideal for feeding an LLM). Set dynamic:true to render JavaScript in a headless browser for SPAs and dynamic pages (costs 5 credits instead of 1), premium:true for hard-to-scrape sites (residential proxies, 10 credits), and country to geotarget the proxy. Example: scrapingdog_scrape({ url: "https://example.com", format: "markdown", dynamic: true, _apiKey: "your-key" })

Safety Scrub — Redact sensitive data from text before logging it or sending it to a model. Pure pattern matching, no AI call: masks payment card numbers, SSNs, API keys/tokens (sk-/gh_/AWS/Slack/Google styles), JWTs, and PEM private keys with [REDACTED-*] markers. Use whenever user-supplied or scraped text may carry credentials. Input: {text: string}. Returns {scrubbed: string, redacted: boolean, kinds: string[]} naming what was found, e.g. ['card','ssn']. Best-effort, not a guarantee. (1 MESH/call, a tool · safety)

Scrape a single web page through a residential proxy and return it as clean Markdown (or HTML/text). Uses a real Chrome TLS fingerprint by default and only spins up a headless browser if the page is bot-challenged. Optionally run structured extraction (CSS selectors) or AI extraction (natural-language prompt). Markdown keeps the complete page by default (content_mode 'smart': everything except nav/footer/cookie chrome, with GFM tables and absolutized links); to inspect a page's raw no-JS/SEO fallback use format 'html'.

Look at a page ONCE with an LLM and get back CSS selectors that extract the fields you asked for. Pass the returned `parser` as the `extract` argument on every later scrape of that same layout and no AI runs again — it becomes a plain, free, deterministic extraction. Use this instead of ai_prompt whenever you will scrape more than a couple of pages of the same shape. Every selector is run against the page before being returned, so `report`/`coverage` tell you which fields are actually reliable.

Store a generated parser under a name so it can be reused by id. Scrape later with scrape's `preset_id` instead of repeating the selectors, and every run is scored per field — when the recent success rate decays (the site redesigned), the preset regenerates itself from `source_url` and bumps a version. Give it a source_url whenever you can: without one it can never self-heal.

Scrape many URLs asynchronously with shared options. Returns a job id — poll with batch_status. For SEO/status audits over many pages set mode 'summary': items carry metadata only (title, description, canonical, contentLength) instead of full page content.

Build a structured dataset from a plain-language prompt. Quantic AI plans the search queries, searches Google/Bing/DuckDuckGo, maps the sites it finds and scrapes them into validated rows (CSV/JSON). Returns a job id — poll with dataset_status. Billed per delivered, validated record (email/phone fields cost extra, only when found); the run never exceeds limits.max_cost_usd, and the unspent budget is refunded.

List the ready-made Collectors: paid, versioned scrapers you run with a semantic input (keyword + location, place id, product id, domain…) instead of URLs — e.g. web_search, search_images, search_videos, keyword_ideas, amazon_search, amazon_product, ebay_search, aliexpress_search, linkedin_jobs, indeed_jobs, reddit_posts, youtube_search, youtube_channel, instagram_profile, tiktok_profile, tiktok_video, linkedin_profile, linkedin_company, zillow_search, zillow_property, app_store_apps, app_store_reviews, google_play_apps, google_maps_places, place_reviews, google_jobs, google_news, google_shopping, product_offers, hotels, google_flights, google_events, google_trends, google_autocomplete, google_lens, youtube_video, ebay_product, flipkart_search, idealista_search, kleinanzeigen_search, autotrader_search, github_repos, hacker_news, coingecko_coins, wikipedia_articles, yahoo_finance, stackoverflow, steam, npm_packages, sec_filings, defillama, wayback_machine, clinical_trials, certificate_transparency, wikidata, nvd_cve, openfda, openalex, pypi_packages, exchange_rates, gleif_lei, docker_hub, crates_io, world_bank, openlibrary_books, arxiv_papers, weather_forecast, whois_domain, dns_records, itunes_search, local_business_leads, site_contacts, company_profile, business_directory. Returns each collector's slug, input/output schema, example input, price per delivered result and current health. Billing is pay-per-success: only delivered rows are charged.

Use this when you need keyword ideas, SEO research, or content planning data for a topic. Returns keyword suggestions and intent data in JSON. Returns: 1. suggestions array (Google autocomplete) 2. relatedQueries with modifiers (vs, for, how to, best, what is) 3. longTailKeywords array 4. searchIntent classification (informational/transactional/navigational/commercial) 5. totalKeywords count. Example output: {"query":"bitcoin trading","suggestions":["bitcoin trading platform","bitcoin trading bot","bitcoin trading for beginners"],"relatedQueries":{"vs":["bitcoin trading vs investing"],"howTo":["how to start bitcoin trading"]},"longTailKeywords":["best bitcoin trading app 2026","bitcoin trading strategy for beginners"],"searchIntent":"commercial","totalKeywords":47} Use this BEFORE writing blog posts, FOR SEO content strategy, topic cluster planning, content gap analysis, and competitive keyword research. Do NOT use for page SEO audit -- use seo_audit_page instead. Do NOT use for content extraction -- use web_scrape_to_markdown instead. Do NOT use for domain intelligence -- use domain_lookup_intelligence instead.

Use this when you need keyword ideas, SEO research, or content planning data for a topic. Returns keyword suggestions and intent data in JSON. POST variant of seo_research_keywords -- same params passed as JSON body instead of query string. Returns: 1. suggestions array (Google autocomplete) 2. relatedQueries with modifiers (vs, for, how to, best, what is) 3. longTailKeywords array 4. searchIntent classification (informational/transactional/navigational/commercial) 5. totalKeywords count. Example output: {"query":"bitcoin trading","suggestions":["bitcoin trading platform","bitcoin trading bot","bitcoin trading for beginners"],"relatedQueries":{"vs":["bitcoin trading vs investing"],"howTo":["how to start bitcoin trading"]},"longTailKeywords":["best bitcoin trading app 2026","bitcoin trading strategy for beginners"],"searchIntent":"commercial","totalKeywords":47} Use this BEFORE writing blog posts, FOR SEO content strategy, topic cluster planning, content gap analysis, and competitive keyword research. Do NOT use for page SEO audit -- use seo_audit_page instead. Do NOT use for content extraction -- use web_scrape_to_markdown instead. Do NOT use for domain intelligence -- use domain_lookup_intelligence instead.

Use this when you need to execute Python, JavaScript, or SQL code in a sandboxed environment and get the output. Supports Python (subprocess), JavaScript (eval), and SQL (in-memory SQLite). 1. output: stdout captured from code execution (max 10KB) 2. language: the language that was executed 3. executionTimeMs: execution duration in milliseconds 4. exitCode: process exit code (0 = success) 5. error: error message if execution failed (null on success) Example output: {"output":"Hello World\n42\n","language":"python","executionTimeMs":234,"exitCode":0,"error":null} Use this FOR running calculations, data transformations, validating code snippets, or querying in-memory databases. Essential when you need computed results rather than static data. Do NOT use for persistent file storage -- sandbox is ephemeral. Do NOT use for generating hashes -- use crypto_generate_hash. Do NOT use for PDF generation -- use document_generate_pdf. Do NOT use for web scraping -- use web_scrape_to_markdown.

Use this when you need to convert HTML to clean Markdown text. Returns the converted markdown in JSON. Returns: 1. markdown (converted text) 2. inputLength (character count of HTML) 3. outputLength (character count of markdown) 4. strippedElements (scripts, styles, iframes removed). Example output: {"markdown":"# Hello World\n\nThis is **bold** and *italic*.\n\n[Link](https://example.com)","inputLength":156,"outputLength":68,"strippedElements":["script","style"]} Use this FOR converting web content to LLM-friendly format, migrating CMS content, cleaning up HTML emails, and preparing content for markdown-based systems. Do NOT use for markdown to HTML -- use text_convert_markdown_to_html instead. Do NOT use for web scraping (fetching + converting) -- use web_scrape_to_markdown instead. Do NOT use for PDF generation -- use document_generate_pdf instead.

Use this when you need to shorten a long URL into a compact link. Accepts any valid URL and returns a shortened URL with the hash code, original URL, and creation timestamp. Do NOT use for domain intelligence — use domain_lookup_intelligence instead. Do NOT use for web scraping — use web_scrape_to_markdown instead. Do NOT use for SEO analysis — use seo_audit_page instead.

Build a source-linked contractor-verification checklist for up to 12 supported states. Returns official lookup links, scope boundaries, and freshness caveats; it does not scrape, cache, or resell license records.

Use this when you need to verify a factual claim, check if a statement is true, or find evidence supporting or contradicting a claim. Accepts a claim text and returns relevant web snippets, source URLs, and a confidence assessment. Do NOT use for general web scraping — use web_scrape_to_markdown instead. Do NOT use for SEO analysis — use seo_audit_page instead. Do NOT use for sentiment analysis — use sentiment_analyzer instead.

Use this when you need to convert CSV data to a JSON array. Returns parsed data with auto-detected settings in JSON. Returns: 1. data (array of objects with column names as keys) 2. rowCount 3. columnCount 4. detectedDelimiter 5. headers array. Example output: {"data":[{"name":"Alice","age":"30","city":"Paris"},{"name":"Bob","age":"25","city":"London"}],"rowCount":2,"columnCount":3,"detectedDelimiter":",","headers":["name","age","city"]} Use this FOR data migration, importing spreadsheets into APIs, converting exported CSV reports, and preparing tabular data for analysis. Do NOT use for JSON validation -- use data_validate_json instead. Do NOT use for markdown conversion -- use text_convert_markdown_to_html instead. Do NOT use for web scraping -- use web_scrape_to_markdown instead.

Use this when you need to determine the emotional tone and sentiment of text. Returns structured sentiment analysis with emotion breakdown and key drivers. 1. sentiment: overall sentiment label (positive, negative, neutral) 2. confidence: confidence score 0-100 3. emotions: detected emotions with scores (joy, anger, fear, surprise, sadness) 4. keyPhrases: array of phrases driving the sentiment 5. score: numeric sentiment score from -1.0 (negative) to 1.0 (positive) Example output: {"sentiment":"positive","confidence":87,"score":0.73,"emotions":{"joy":0.82,"surprise":0.15,"anger":0.01,"fear":0.01,"sadness":0.01},"keyPhrases":["excellent results","exceeded expectations"]} Use this BEFORE responding to customer feedback, reviews, or social media mentions. Essential for brand monitoring, support ticket triage, and content tone analysis. Do NOT use for summarization -- use ai_summarize_text. Do NOT use for content extraction -- use web_scrape_to_markdown. Do NOT use for text classification -- use text_classify_content.

Use this when you need to test a webhook endpoint by sending a request with custom headers, body, and method. Returns full response data in JSON. Returns: 1. statusCode 2. responseBody (parsed JSON or text) 3. responseHeaders (key-value map) 4. latency in ms 5. tlsInfo (protocol, cipher) 6. method used 7. requestSize and responseSize in bytes. Example output: {"url":"https://hooks.example.com/callback","method":"POST","statusCode":200,"responseBody":{"received":true},"latency":142,"tlsInfo":{"protocol":"TLSv1.3","cipher":"AES-256-GCM"},"requestSize":256,"responseSize":48} Use this FOR testing webhook integrations, debugging API callbacks, validating endpoint availability, and measuring webhook response times. Do NOT use for web scraping -- use web_scrape_to_markdown instead. Do NOT use for screenshot capture -- use capture_screenshot instead. Do NOT use for HTTP header security audit -- use network_analyze_headers instead.

Use this when you need a comprehensive research report on any topic. Accepts a topic and optional depth parameter. Fetches multiple web sources, extracts key information, and compiles a structured report with summary, key findings, sources, and analysis. Returns markdown-formatted report. Do NOT use for simple web search — use web_search_query instead. Do NOT use for single page scraping — use web_scrape_to_markdown instead. Do NOT use for SEO analysis — use seo_audit_page instead.

Cryptographically verify a Web Bot Auth signature (RFC 9421) — proof of WHICH agent is calling, not a guess from the user-agent. Returns verified true/false plus the agent's identity and declared purpose. This is the only check here that proves rather than infers, so it carries no score and no verdict: a verified assistant acting for a real user is usually welcome, a verified scraper usually is not, and that policy is the caller's. Pass the request the agent made to YOU — the signature covers its method, authority and path.

Use this when you need to identify what technologies a website uses. Returns structured JSON with categorized detections, confidence levels, and evidence for each technology found. 1. technologies (array) -- each entry has name, category, confidence, evidence 2. categories detected: CMS (WordPress, Shopify, Wix, Webflow), JS Frameworks (React, Next.js, Vue, Angular, Svelte), CSS (Tailwind, Bootstrap), Analytics (Google Analytics, GTM, Facebook Pixel, Hotjar, Mixpanel), Hosting (Vercel, Netlify, AWS, Railway), CDN (Cloudflare), Payments (Stripe, PayPal), Support (Intercom, Crisp, Zendesk) 3. summary.totalDetected (number) -- count of technologies found 4. summary.categories (object) -- count per category Example output: {"technologies":[{"name":"React","category":"JS Framework","confidence":"high","evidence":"react-dom in bundle"},{"name":"Tailwind CSS","category":"CSS","confidence":"high","evidence":"tailwind classes in HTML"},{"name":"Vercel","category":"Hosting","confidence":"medium","evidence":"x-vercel-id header"}],"summary":{"totalDetected":8,"categories":{"JS Framework":2,"CSS":1,"Hosting":1,"Analytics":3,"Payments":1}}} Use this BEFORE sales outreach (to personalize pitch by tech stack), competitive analysis, or security audits. Essential for qualifying leads by technology fit and understanding competitor architecture. Do NOT use for full company data -- use company_enrich_from_domain instead. Do NOT use for SEO analysis -- use seo_audit_page instead. Do NOT use for website content -- use web_scrape_to_markdown instead.

Use this when you need to identify what technologies a website uses. Returns structured JSON with categorized detections, confidence levels, and evidence for each technology found. 1. technologies (array) -- each entry has name, category, confidence, evidence 2. categories detected: CMS (WordPress, Shopify, Wix, Webflow), JS Frameworks (React, Next.js, Vue, Angular, Svelte), CSS (Tailwind, Bootstrap), Analytics (Google Analytics, GTM, Facebook Pixel, Hotjar, Mixpanel), Hosting (Vercel, Netlify, AWS, Railway), CDN (Cloudflare), Payments (Stripe, PayPal), Support (Intercom, Crisp, Zendesk) 3. summary.totalDetected (number) -- count of technologies found 4. summary.categories (object) -- count per category Example output: {"technologies":[{"name":"React","category":"JS Framework","confidence":"high","evidence":"react-dom in bundle"},{"name":"Tailwind CSS","category":"CSS","confidence":"high","evidence":"tailwind classes in HTML"},{"name":"Vercel","category":"Hosting","confidence":"medium","evidence":"x-vercel-id header"}],"summary":{"totalDetected":8,"categories":{"JS Framework":2,"CSS":1,"Hosting":1,"Analytics":3,"Payments":1}}} Use this BEFORE sales outreach (to personalize pitch by tech stack), competitive analysis, or security audits. Essential for qualifying leads by technology fit and understanding competitor architecture. Do NOT use for full company data -- use company_enrich_from_domain instead. Do NOT use for SEO analysis -- use seo_audit_page instead. Do NOT use for website content -- use web_scrape_to_markdown instead.

Check whether a HeySale salesperson has actually been installed on its website and is reporting back to HeySale. Uses HeySale's own telemetry, so it works with single-page apps, tag managers and deferred script loading — never scrape the website's HTML to answer this. Give it the salesperson_id returned when the salesperson was created.

Use this when you need to extract text from an image. Accepts an image URL or base64-encoded image data. Returns the extracted text, word count, confidence score, and detected language. Do NOT use for web page text extraction — use web_scrape_to_markdown instead. Do NOT use for PDF text extraction — use document_generate_pdf instead. Do NOT use for taking screenshots — use capture_screenshot instead.

Publish a post to Bluesky as the connected account. Text up to 300 characters — Bluesky ALSO caps a post at 3000 UTF-8 bytes, so an emoji-heavy post can be under 300 characters and still be refused; Hermoso checks both before spending the round trip and says which limit and by how much. MEDIA: either up to 4 images (imageUrls + altText) OR one MP4 video (videoUrl + videoAlt), never both — a Bluesky post record carries a single embed and images and video are two different embed types. Video is MP4 only, up to 300MB at Bluesky's end (Hermoso can fetch up to 150MB from a URL), with optional WebVTT caption tracks; the aspect ratio is measured from the file. Bluesky requires a CONFIRMED EMAIL on the account before it will process any video — if it is unconfirmed you get a refusal saying so, and reconnecting will not help. Links in the text are made clickable automatically. LINK CARDS: Bluesky does NOT scrape links, so a URL posted bare renders as plain blue text — the client composing the post has to build the card. Hermoso builds one AUTOMATICALLY when the post has a URL and NO media: it fetches the page, uses its title/description and uploads its image as the card thumbnail. Pass linkCard:false to suppress it, or linkCard:{uri,title,description,thumbUrl} to control it (give both title and description and the page is not fetched at all). A POST CARRIES ONE EMBED, so a card and images/video cannot both ride: if you pass linkCard explicitly ALONGSIDE media the call is REFUSED by name rather than silently dropping one, and if the URL was merely in the text the MEDIA WINS and the reply says the card was skipped (the link stays clickable either way). Returns the post's public bsky.app URL. Connect at Settings ▸ Connectors ▸ Bluesky with a handle and an APP PASSWORD.

Lock an image as the ad's real PRODUCT photo and SAVE it as this brand's default product, so every later plan_ad / render_ad / generate_image grounds on the true packaging without being told again. Pass `imageUrl` = a product shot's URL — an image from a prior research result (an organic Instagram/TikTok post, a scraped page image), a workspace / list_product_photos url, or any public product photo. The server downloads it and runs a product+safety check: a lifestyle/scene shot with no clear product, or an off-category / unsafe image, is REJECTED and NOTHING is locked or saved (the summary says why). On PASS it persists the photo to a DURABLE url, writes it to the brand's product library as the DEFAULT, and READS THE BRAND BACK to confirm — `savedToBrand` and the summary report what the brand ACTUALLY holds now, never what was asked for, so if it did not become the default you are told instead of finding out from a paid render. Bills one vision check. Reads YOUR saved brand for the category match (pass brandId to target a specific brand — switches this key's active brand like use_brand).

Checks whether a value you did not fetch yourself is actually on the source page right now, and returns a signed pass/fail/unverifiable verdict. Not a retrieval tool: it checks a value you already have, it does not find data for you. Use it when you hold a value that came from somewhere else (a search result, a scraper, an upstream API, another agent) and you need to know it is on the source page before you act on it. Takes the URL, the claimed value, and what was asked; it independently re-fetches the page and never trusts the fetch you were given. Structural guarantee: a claim is never certified unless the re-fetched page contains it, and the model judge can only veto a pass, never create one. Anything unconfirmed returns unverifiable, never pass. Every verdict carries a stable verdict_id, the engine digest that produced it, and a signature verifiable offline against the public key (see get_verifier_info). Scope: server-rendered pages; JS-only content returns unverifiable rather than a guess. $0.01 per check, first 100 free, paid in-band via x402.

Scrape a single URL with AXIS's owned crawler (SSRF-guarded fetch, robots.txt-aware, readability extraction — no third-party key) and return markdown-formatted content. Honest scope: fetches static HTML only, no JavaScript rendering, so client-rendered SPA pages may extract thin content. Returns markdown body, extracted metadata, and title. Best for research, documentation reading, or SEO analysis. Requires Authorization: Bearer <api_key>. Pricing: $0.10 per page. Lite mode carries no discount here — the markdown output is identical in both modes. If the operator's backend configuration is incomplete, this call returns {_not_configured:true} instead of scraping, and is not billed. Use iliad_web_research_crawl for crawling multiple pages or link following. To make a scraped page searchable later, index it with iliad_web_search.

Crawl a domain with AXIS's owned crawler — a same-origin BFS frontier with robots.txt compliance and per-host politeness, no third-party key — and scrape multiple pages. Honest scope: static HTML only, no JavaScript rendering. Returns array of scraped pages with markdown content. Best for site mapping, content audits, or bulk research. Requires Authorization: Bearer <api_key>. Pricing: $0.01/page beyond your account's shared 100-page/month free pool (standard and lite) — a crawl fully covered by the free pool costs $0.00; a fully-paid 100-page crawl costs up to $1.00. If the operator's backend configuration is incomplete, this call returns {_not_configured:true} instead of crawling, and is not billed. Use iliad_web_research for single-page scrapes. To make crawled pages searchable later, index them with iliad_web_search.

AXIS-owned BM25 search engine over the corpus YOUR account has indexed. NOT a Google/Bing scraper — agents build their own searchable index by first calling operation='index' with documents (often pages fetched via iliad_web_research), then querying with operation='search'. Five operations: `index` (insert one or many documents), `search` (BM25 top-k ranked hits with snippet + score + metadata), `delete` (drop one doc), `delete_namespace` (drop all), `count`. Namespaces are account-scoped server-side (`acct:<id>:<namespace>`). Persistent across restarts via Postgres (the shared @axis/snapshots database). Search supports `max_results` (default 10, max 100) and `site` (restrict to a single URL host, case-insensitive). Only operation='search' is billed ($0.01 standard, free in lite mode) — index, delete, delete_namespace, and count are always free. Engineer mode (X-Agent-Mode: engineer — Answer Engine, $0.25): search also returns a grounded extractive answer with [n] citation spans over your corpus, reranked, refusing on weak evidence. Requires Authorization: Bearer <api_key>.

Full web scraping, search, and browser automation (Firecrawl-backed). Sync reads: 'scrape' (URL → markdown/HTML/screenshot/links), 'search' (web search with page content), 'map' (discover a site's URLs), 'extract' (LLM structured data from a page). Async jobs (submit, then poll with job_get/job_list, stop with job_cancel): 'crawl' a whole site, 'batch_scrape' many URLs, 'agent' (autonomous multi-step web task). Change detection: monitor_create/list/get/update/delete/checks (re-check a URL on a cadence → webhook). Interactive browser: session_create/get/list, session_execute (run code in the live page), session_close. Pricing varies by action (scrape 1cr, search 2cr, extract/session_create 5cr, agent 25cr; jobs/monitors/sessions management 0cr) — see the 'web' get_docs topic. Requires a valid SOCIALCRAWL_API_KEY.