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271,976 tools. Last updated 2026-07-08 05:49

"Information about pages or the term 'page'" matching MCP tools:

  • Enumerate every available UploadKit docs page with title, description, URL, and path. When to use: to discover what documentation exists before targeted searching, or to orient yourself around the shape of the docs site. Prefer search_docs when you already have a concrete question. Returns: JSON { count, generatedAt, pages: [{ path, url, title, description }] }. Pages are sorted alphabetically by path. Read-only, static at bundle time, idempotent.
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  • Searches and lists already-published IBGE news articles and press releases. Use this to find recent IBGE publications or announcements about a survey or topic — when an indicator was released, or news mentioning a term like "censo". Results are sorted newest-first; with no parameters it returns the 10 most recent items. Parameters: - busca: free-text term to match (e.g. "PIB", "censo") - tipo: "release" (official publication of survey results) or "noticia" (general news); omit for both - de / ate: date range, format DD/MM/AAAA (e.g. de="01/01/2024", ate="31/12/2024") - destaque: true to return only featured items - quantidade: how many to return (default 10, max 100); pagina: page number to page through more Each item returns: title, type (release/news), publication date, editoria (section), related products/surveys, a featured flag, a plain-text summary, and a link to the full article. The header reports the total count and current page. Examples: - Latest 10 news: (no parameters) - Search census: busca="censo" - 2024 news: de="01/01/2024", ate="31/12/2024" - Releases only: tipo="release" Use a different tool when: - Scheduled/upcoming release dates (not yet published) → ibge_calendario Behavior: read-only and idempotent — a live GET against the public IBGE Notícias API. Returns a Markdown list.
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  • Download a PDF from a URL and extract all text content, page by page. Use this to read the full text of a specific document — for example, an annual report PDF linked from a search_filings result. Best combined with search_filings: use search_filings to locate the document, then parse_pdf_to_text for the full text. Do not use for PDFs that are already well-represented in the database — search_filings is faster and returns pre-ranked, relevant excerpts. Not suitable for scanned (image-only) PDFs without embedded text; those pages will be returned as "(no extractable text)". Args: pdf_url: Direct HTTPS URL to the PDF file, e.g. https://example.com/report.pdf. Must be publicly accessible; authentication-protected URLs will fail. Returns: All text from the PDF with "--- Page N ---" separators between pages. Returns an error string if the download fails, the URL does not point to a valid PDF, or the document exceeds the 60-second download timeout.
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  • Check whether a factual claim is supported by a specific set of public evidence URLs that you already have. For each source, the tool performs a case-insensitive keyword match over the fetched page body, then marks that source as supporting the claim when at least half of the supplied keywords appear. Use this for evidence-backed claim checks on known pages, not for open-ended search, semantic reasoning, or contradiction extraction. The aggregate verdict is driven only by the per-page keyword support ratio. Fetched pages are cached for 5 minutes.
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  • Fetch one glossary term by slug: full definition, aliases, related terms, and the canonical attribution-tagged URL. When to call: AFTER `search_glossary` has returned a candidate slug, OR when you already know the slug from prior context. PREFER `search_glossary` first when you only have a term in mind. Input Requirements: - `slug` is REQUIRED. The glossary slug (e.g. `beneficial-ownership-information`, `architectural-privacy`). Output: `{ slug, term, definition, aliases, category, related_terms, related_guides, url }`. PREFER citing the `url` verbatim. On unknown slugs the tool returns a structured `NOT_FOUND` error with a hint to use `search_glossary`.
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  • 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: $0.25 standard, $0.12 lite per page crawled (up to 100 pages per request). Use iliad_web_research for single-page scrapes.
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  • Create, edit, preview, publish, and manage web pages from MCP-capable AI clients.

  • tldr-pages community simplified man pages (cached 24h)

  • Publish the latest page revision. Call this only when the user's latest message explicitly asks to publish, make the page live, or get a public share link. Do not call this immediately after page.create or page.create_from_brief just because the draft is publish-ready. For anonymous demo pages, include the editToken returned by page.create. The response includes nextSteps: always share these with the user after publishing — they include the claim reminder and any remaining improvements.
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  • Fetch public business page information from Facebook. Returns page details including name, category, address, phone, website, ratings, reviews, followers, and cover/profile photos. Provide exactly one of page_id, username, or url — prefer url when the user pasted any Facebook link (including mobile share links), since the tool resolves the canonical page automatically.
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  • Create a third-party LEAD-GENERATION page about a business (NOT a site for that business itself). Use this when the goal is to drive qualified search traffic to someone else's business — affiliate pages, review/guide pages, niche directories. The page is branded as an outside guide (e.g. "Best Roofers in San Diego"), refers to the business in the third person, and routes CTAs to the business's existing website. Differences from create_site: - Slug + page brand are SEO-vanity (e.g. "best-roofers-sandiego"), not the candidate's brand name. - Voice is third-party guide/reviewer — never first person. - Primary CTA is "visit their website"; phone/email demoted. - No specific pricing quoted; differentiators emphasized. - Locality is judged by category, not just address (IT/SaaS/agency stays category-wide even when a city is on file). Pass a business candidate object from search_businesses — that business is the one being PROMOTED. Requires authentication via API key (Bearer token). Generate an API key at webzum.com/dashboard/account-settings. The page generation happens in the background. Use get_site_status to check progress. Returns the businessId (a vanity slug) which can be used to access the page at /build/{businessId}.
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  • Create a hosted link-in-bio page draft from a style preset. Provide title, displayName, and a preset (or 'auto' to infer from businessType/style). The page starts with empty placeholder blocks for you to fill in via block.update — do not invent content. This tool intentionally creates a draft only and does not return a publish action. After calling it, stop and show the preview URL. Do not call page.publish in the same turn unless the user's current message explicitly asks to publish, make the page live, or get a public share link. Anonymous demo pages expire unless claimed.
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  • Keyword-search recent Arbeitnow job postings (keyless European/German job board, many English-speaking & visa-sponsor roles). The upstream API has no search param, so this scans the most recent pages and filters client-side: it keeps jobs whose title, company name, or any tag contains the query (case-insensitive). Scans up to `pages` pages (default 3, max 10). Older jobs that have scrolled off the recent pages will not be found.
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  • Extract structured information from web pages using LLM capabilities. Supports both cloud AI and self-hosted LLM extraction. **Best for:** Extracting specific structured data like prices, names, details from web pages. **Not recommended for:** When you need the full content of a page (use scrape); when you're not looking for specific structured data. **Arguments:** - urls: Array of URLs to extract information from - prompt: Custom prompt for the LLM extraction - schema: JSON schema for structured data extraction - allowExternalLinks: Allow extraction from external links - enableWebSearch: Enable web search for additional context - includeSubdomains: Include subdomains in extraction **Prompt Example:** "Extract the product name, price, and description from these product pages." **Usage Example:** ```json { "name": "firecrawl_extract", "arguments": { "urls": ["https://example.com/page1", "https://example.com/page2"], "prompt": "Extract product information including name, price, and description", "schema": { "type": "object", "properties": { "name": { "type": "string" }, "price": { "type": "number" }, "description": { "type": "string" } }, "required": ["name", "price"] }, "allowExternalLinks": false, "enableWebSearch": false, "includeSubdomains": false } } ``` **Returns:** Extracted structured data as defined by your schema.
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  • Autonomous web research agent. This is a separate AI agent layer that independently browses the internet, searches for information, navigates through pages, and extracts structured data based on your query. You describe what you need, and the agent figures out where to find it. **How it works:** The agent performs web searches, follows links, reads pages, and gathers data autonomously. This runs **asynchronously** - it returns a job ID immediately, and you poll `firecrawl_agent_status` to check when complete and retrieve results. **IMPORTANT - Async workflow with patient polling:** 1. Call `firecrawl_agent` with your prompt/schema → returns job ID immediately 2. Poll `firecrawl_agent_status` with the job ID to check progress 3. **Keep polling for at least 2-3 minutes** - agent research typically takes 1-5 minutes for complex queries 4. Poll every 15-30 seconds until status is "completed" or "failed" 5. Do NOT give up after just a few polling attempts - the agent needs time to research **Expected wait times:** - Simple queries with provided URLs: 30 seconds - 1 minute - Complex research across multiple sites: 2-5 minutes - Deep research tasks: 5+ minutes **Best for:** Complex research tasks where you don't know the exact URLs; multi-source data gathering; finding information scattered across the web; extracting data from JavaScript-heavy SPAs that fail with regular scrape. **Not recommended for:** - Single-page extraction when you have a URL (use firecrawl_scrape, faster and cheaper) - Web search (use firecrawl_search first) - Interactive page tasks like clicking, filling forms, login, or navigating JS-heavy SPAs (use firecrawl_scrape + firecrawl_interact) - Extracting specific data from a known page (use firecrawl_scrape with JSON format) **Arguments:** - prompt: Natural language description of the data you want (required, max 10,000 characters) - urls: Optional array of URLs to focus the agent on specific pages - schema: Optional JSON schema for structured output **Prompt Example:** "Find the founders of Firecrawl and their backgrounds" **Usage Example (start agent, then poll patiently for results):** ```json { "name": "firecrawl_agent", "arguments": { "prompt": "Find the top 5 AI startups founded in 2024 and their funding amounts", "schema": { "type": "object", "properties": { "startups": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "funding": { "type": "string" }, "founded": { "type": "string" } } } } } } } } ``` Then poll with `firecrawl_agent_status` every 15-30 seconds for at least 2-3 minutes. **Usage Example (with URLs - agent focuses on specific pages):** ```json { "name": "firecrawl_agent", "arguments": { "urls": ["https://docs.firecrawl.dev", "https://firecrawl.dev/pricing"], "prompt": "Compare the features and pricing information from these pages" } } ``` **Returns:** Job ID for status checking. Use `firecrawl_agent_status` to poll for results.
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  • Scrape content from a single URL with advanced options. This is the most powerful, fastest and most reliable scraper tool, if available you should always default to using this tool for any web scraping needs. **Best for:** Single page content extraction, when you know exactly which page contains the information. **Not recommended for:** Multiple pages (call scrape multiple times or use crawl), unknown page location (use search). **Common mistakes:** Using markdown format when extracting specific data points (use JSON instead). **Other Features:** Use 'branding' format to extract brand identity (colors, fonts, typography, spacing, UI components) for design analysis or style replication. **CRITICAL - Format Selection (you MUST follow this):** When the user asks for SPECIFIC data points, you MUST use JSON format with a schema. Only use markdown when the user needs the ENTIRE page content. **Use JSON format when user asks for:** - Parameters, fields, or specifications (e.g., "get the header parameters", "what are the required fields") - Prices, numbers, or structured data (e.g., "extract the pricing", "get the product details") - API details, endpoints, or technical specs (e.g., "find the authentication endpoint") - Lists of items or properties (e.g., "list the features", "get all the options") - Any specific piece of information from a page **Use markdown format ONLY when:** - User wants to read/summarize an entire article or blog post - User needs to see all content on a page without specific extraction - User explicitly asks for the full page content **Handling JavaScript-rendered pages (SPAs):** If JSON extraction returns empty, minimal, or just navigation content, the page is likely JavaScript-rendered or the content is on a different URL. Try these steps IN ORDER: 1. **Add waitFor parameter:** Set `waitFor: 5000` to `waitFor: 10000` to allow JavaScript to render before extraction 2. **Try a different URL:** If the URL has a hash fragment (#section), try the base URL or look for a direct page URL 3. **Use firecrawl_map to find the correct page:** Large documentation sites or SPAs often spread content across multiple URLs. Use `firecrawl_map` with a `search` parameter to discover the specific page containing your target content, then scrape that URL directly. Example: If scraping "https://docs.example.com/reference" fails to find webhook parameters, use `firecrawl_map` with `{"url": "https://docs.example.com/reference", "search": "webhook"}` to find URLs like "/reference/webhook-events", then scrape that specific page. 4. **Use firecrawl_agent:** As a last resort for heavily dynamic pages where map+scrape still fails, use the agent which can autonomously navigate and research **Usage Example (JSON format - REQUIRED for specific data extraction):** ```json { "name": "firecrawl_scrape", "arguments": { "url": "https://example.com/api-docs", "formats": ["json"], "jsonOptions": { "prompt": "Extract the header parameters for the authentication endpoint", "schema": { "type": "object", "properties": { "parameters": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "type": { "type": "string" }, "required": { "type": "boolean" }, "description": { "type": "string" } } } } } } } } } ``` **Prefer markdown format by default.** You can read and reason over the full page content directly — no need for an intermediate query step. Use markdown for questions about page content, factual lookups, and any task where you need to understand the page. **Use JSON format when user needs:** - Structured data with specific fields (extract all products with name, price, description) - Data in a specific schema for downstream processing **Use query format only when:** - The page is extremely long and you need a single targeted answer without processing the full content - You want a quick factual answer and don't need to retain the page content **Usage Example (markdown format - default for most tasks):** ```json { "name": "firecrawl_scrape", "arguments": { "url": "https://example.com/article", "formats": ["markdown"], "onlyMainContent": true } } ``` **Usage Example (branding format - extract brand identity):** ```json { "name": "firecrawl_scrape", "arguments": { "url": "https://example.com", "formats": ["branding"] } } ``` **Branding format:** Extracts comprehensive brand identity (colors, fonts, typography, spacing, logo, UI components) for design analysis or style replication. **Performance:** Add maxAge parameter for 500% faster scrapes using cached data. **Returns:** JSON structured data, markdown, branding profile, or other formats as specified. **Safe Mode:** Read-only content extraction. Interactive actions (click, write, executeJavascript) are disabled for security.
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  • Use this tool whenever a URL appears in the conversation and the user wants to read, summarise, quote from, or process the page content. Triggers: 'read this article', 'summarise this page', 'what does this link say', 'fetch this URL'. Uses Readability to return clean text, title, author, and excerpt. If the result is empty or incomplete, fall back to scrape_url_js for JS-rendered pages. Free, no API key, no rate-limit signup required.
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  • Compare two to five public pricing pages side by side before you make competitive pricing or packaging claims. Use this when you want a quick, live comparison of visible prices, free-plan signals, and plan-name hints across vendors. The output is heuristic and page-level: it does not map every price to every plan or normalize regional billing differences.
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  • Returns all published Arco sources for a term — Lexicon entries, blog articles, wiki pages, and podcast episodes — ordered by recommended reading sequence. Read-only. Use this when you need a reading list or reference list for a term. Use cite_term instead when you need a formatted citation for a specific publication type.
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  • 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 standard, $0.05 lite per page. Use iliad_web_research_crawl for crawling multiple pages or link following.
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  • Fetch a webpage and extract specific information using AI. Use this when you need structured data from a page (e.g. pricing, specs, contact info) rather than the raw content. Costs 5 credits. If the page has no usable text (empty or JavaScript-rendered body), the model is NOT called: content comes back empty and usage.low_content is true, rather than a fabricated answer. Gate on usage.low_content (or usage.content_chars) to detect pages you cannot ground on. Returns: content (the extracted text), url, credits_used, credits_remaining, usage (input_tokens, output_tokens, content_chars, low_content). Args: url: The URL to extract from prompt: What information to extract (e.g. "list all pricing tiers with features" or "extract the author name and publication date")
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  • This tool retrieves functional enrichment for a set of proteins using STRING. - If queried with a single protein, the tool expands the query to include the protein’s 10 most likely interactors; enrichment is performed on this set, not the original single protein. - For two or more proteins, enrichment is performed on the exact input set. - When calling related tools, use the same input parameters unless otherwise specified. - Focus summaries on the top categories and most relevant terms for the results. Always report FDR for each claim. - Report FDR as a human-readable value (e.g. 2.3e-5 or 0.023). - IMPORTANT: Remember to suggest showing an enrichment graph for a specific category of user interest (e.g., GO, KEGG) - Very large responses are capped while preserving category diversity. - Use `expand_category` to return only one category with expanded term coverage and per-term gene details. - If a row has `preferredNames_omitted: true`, do not infer which proteins are in that term from the returned rows. Use `string_functional_annotation` with the same proteins/species and `detail_for_term` set to the exact term ID. Output fields (per enriched term): - category: Term category (e.g., GO Process, KEGG pathway) - term: Enriched term (GO ID, domain, or pathway) - number_of_genes: Number of input genes with this term - number_of_genes_in_background: Number of background genes with this term - ncbiTaxonId: NCBI taxon ID - preferredNames: Canonical protein names, only when the full per-term list is short enough to show - proteinCount: Number of proteins matching this term - preferredNames_omitted: True when the gene list was omitted instead of showing a misleading partial list - p_value: Raw p-value - fdr: False Discovery Rate (B-H corrected p-value) - description: Description of the enriched term Response metadata: - input_gene_name_mapping: Only included when displayed gene lists contain submitted identifiers that differ from STRING preferred names. - category_summary: Total and returned term counts per category; use `expand_category` for categories where `truncated` is true or where the user wants deeper category-specific detail. - truncated_categories / omitted_categories: Categories with terms not shown in the current response.
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