Tool search 101,866 tools · 5,225 live servers

Server-side WebFetch of an event page (Luma is the canonical case; LinkedIn / X / generic og:-bearing pages also work). Returns parsed { title, date, image, description, organization } so the agent doesn't have to scrape and parse OG / JSON-LD itself. Use the result to compose a HighlightStory. Args: { url }. Returns: a metadata map; empty fields where extraction missed.

Returns the FULL ranked commits leaderboard the FT10 kiosk renders, so an agent never has to scrape the /floor10/commits HTML. Each member carries { rank (1-based), handle, name, commits, private? }. `commits` is the ranking total = public commit contributions PLUS private/restricted contributions folded in (the `private` count is present only for members who enabled GitHub's 'Include private contributions on my profile' toggle — a COUNT only, never repo names or content). Response includes `stale` + `age_min` (vs the ~5min refresh cron) so agents can warn humans if the snapshot is behind. Args: { limit?: number, default 200, max 200 }. Required scope: membership:read.

Semantic web search for finding relevant pages, documents, and current information. Alternative to Exa search at 3x lower cost. Returns structured JSON results with ranked matches, titles, URLs, and text snippets. 1. results (array) -- ranked list of search results 2. results[].title (string) -- page title 3. results[].url (string) -- full URL to the page 4. results[].snippet (string) -- relevant text excerpt with query terms highlighted 5. query (string) -- the search query used 6. totalResults (number) -- number of results returned Example output: {"query":"best CRM for startups 2026","results":[{"title":"Top 10 CRMs for Startups in 2026","url":"https://blog.example.com/crm-startups","snippet":"HubSpot leads the pack for early-stage startups with its free tier..."},{"title":"CRM Comparison Guide","url":"https://review.example.com/crm","snippet":"We tested 15 CRM platforms across pricing, features..."}],"totalResults":5} Use this BEFORE answering questions about current events, finding documentation, researching competitors, or gathering data on any topic. Essential for semantic web search when the agent needs up-to-date information beyond its training data. Drop-in replacement for Exa search. Do NOT use for web page content extraction -- use web_scrape_to_markdown instead. Do NOT use for SEO analysis -- use seo_audit_page instead. Do NOT use for screenshot capture -- use capture_screenshot instead. Do NOT use for company data -- use company_enrich_from_domain instead.

Scrape and extract content from a URL with full JS rendering, returned as clean markdown. Alternative to Firecrawl scrape at 2.5x lower cost. Strips navigation, ads, scripts, and boilerplate — ideal for RAG pipelines and AI research agents. 1. title (string) -- page title from <title> tag 2. description (string) -- meta description 3. author (string) -- author from meta tags or schema 4. content (string) -- clean markdown body text, headings preserved 5. wordCount (number) -- total words in extracted content 6. charCount (number) -- total characters 7. url (string) -- final URL after redirects Example output: {"title":"How to Scale APIs","description":"A guide to...","content":"# How to Scale APIs\n\nScaling requires...","wordCount":1250,"charCount":7800,"url":"https://blog.example.com/scale-apis"} Use this BEFORE summarizing articles, building RAG corpora, researching topics from web sources, or extracting data from documentation pages. Essential for any workflow that needs to scrape and extract content from web pages as LLM input. Drop-in replacement for Firecrawl scrape. Do NOT use for screenshots -- use capture_screenshot instead. Do NOT use for SEO audit -- use seo_audit_page instead. Do NOT use for tech stack detection -- use website_detect_tech_stack instead. Do NOT use for web search -- use web_search_query instead.

Use this when you need to extract clean content from multiple web pages at once (up to 10 URLs). Returns the same structured markdown output as web_scrape_to_markdown for each URL. 1. results (array) -- each entry has title, description, author, content, wordCount, charCount, url 2. summary -- total pages scraped, total word count, failed URLs if any Example output: {"results":[{"url":"https://a.com","title":"Page A","wordCount":800},{"url":"https://b.com","title":"Page B","wordCount":1200}],"summary":{"total":2,"totalWords":2000,"failed":0}} Use this FOR building research corpora, comparing content across competitor pages, or bulk documentation extraction. Essential when you have 3+ URLs to process in one workflow. Do NOT use for single URLs -- use web_scrape_to_markdown instead. Do NOT use for SEO comparison -- use seo_audit_batch instead.

Fetch the fully rendered HTML of any web page through the ScrapeUnblocker API (https://developers.scrapeunblocker.com), bypassing anti-bot protection (Cloudflare, DataDome, PerimeterX, Akamai, Shape). Use when a normal fetch is blocked (403/429, captcha) or the page needs a real browser. Returns raw HTML.

Fetch a web page through the ScrapeUnblocker API (https://developers.scrapeunblocker.com) and return AI-parsed structured JSON instead of raw HTML (product details, article content, listings).

Run a Google search through the ScrapeUnblocker API (https://developers.scrapeunblocker.com) and return organic results as JSON.

AUTHORITATIVE summary of a Wikipedia article by exact title — typically faster + cheaper than search_wikipedia + get_article_sections + scrape. Returns the article's lead paragraph (the editorial overview), one-line description, thumbnail image, and a few related-content links. Use when you already have the canonical title (got it from search_wikipedia, or it's a well-known entity) and need the standard "what is X" prose answer. For the full section breakdown use get_article_sections.

Public / statutory holidays for a country (and optional state/province/region) in a given year — computed in-house, NOT scraped, so it's exact and offline-stable. Per holiday: date, name, weekday, whether it's an observed-day shift, the country, and the subdivision (when one was requested), plus the next upcoming holiday. `country` = ISO-3166 alpha-2 code (default "AU"); common names accepted ("australia", "usa", "uk", "new zealand"). 100+ countries. `year` = 4-digit year (default = current year). `subdiv` = optional subdivision code for region-specific holidays — e.g. "NSW"/"VIC"/"QLD" for AU, "CA"/"NY" for US, "ENG"/"SCT" for GB; national-level only if omitted (state lists are richer). Pairs with `market_hours`. Holiday DATES are non-copyrightable facts; computed via the open-source `holidays` library (vacanza, MIT) — commercial use + redistribution permitted, keyless. Informational only — confirm against the official gazette for legal/payroll use. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

Clean text from URL, alias of text-scrape.

Extract text from URL.

Fetch any public URL and return cleaned text + metadata. POST {"action":"web-fetch","url":"https://..."}. Handles JS-rendered pages via fallback. $0.005/call.

抓取一个公开网页，返回干净的 Markdown（供 AI 阅读/总结/提取）。 只抓公开网页，禁止内网/保留地址；不生成任何内容，只做抓取与格式转换。 静态抓取拿不到内容的 JS 动态页可加 render=true 走真浏览器渲染。

结构化抽取网页要素（表格/链接/图片/标题大纲/元数据/自定义 CSS 选择器），返回 JSON。 与 scrape_url 的区别：不返回整页正文，只返回指定结构——表格转成 headers+rows 的 JSON 可直接计算；配自定义选择器可精准取价格/标题等字段。

发现站点 URL 清单：优先 robots.txt / sitemap.xml，无站点地图时退化为入口页链接发现。 配合 scrape_url / crawl 使用：先 site_map 挑准页面，再精准抓取，省 credit。

Search organizer profiles enriched from event data. Returns organizer names, source, website/contact details, Instagram profile URL plus handle, and scrape status.

Scrape Reddit posts and comments by subreddit, keyword search, or post URL. Returns full post data with comment trees.

Scrape job listings from company ATS boards: Greenhouse, Lever, Workday, and Ashby. No auth required — uses their public job APIs.

Scrape Trustpilot reviews for any company. Returns reviewer name, star rating, review title, body, date, and verified status.

Ask a specific business's ANOTS agent a question, by slug. The agent answers from that business's own knowledge base and tools (live data, not scraped HTML). Get the slug from search_businesses.

Scrape a webpage with custom options

Scrape meta tags from a URL

Extract clean text from any URL