"Crawling Websites to Extract Data" matching MCP tools:

• extract_structured

  DocImprint

  Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, parse_invoice (prebuilt schema) may be simpler. For general summarization, use summarize_document instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."

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• get_job_status

  api

  Poll the status of an async job (extract, indexing, batch). Free — no credits consumed. Use after add_document_to_collection or async extract to check when processing completes. Poll this endpoint in a loop until status is "complete" or "failed". Completed jobs include the bundle_id or result_json in the response. Jobs are created when you POST /v1/extract with a webhook, or when add_document_to_collection triggers async indexing. Returns: { id, type: "extract"|"extract_batch"|"index_collection", status: "queued"|"processing"|"complete"|"failed"|"cancelled", progress_pct: number (0–100), progress_message, bundle_id (when complete), result_json (when complete), error (when failed), created_at, completed_at } Example prompts: - "Check the status of my indexing job job_550e8400." - "Is my async extract job done yet?" - "Poll job [job_id] — what is the current progress?"

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• image_palette

  Colour Memory

  Upload an image (base64 encoded) and extract its dominant colour palette, with each colour matched to its nearest named archive entry with full cultural provenance. Uses K-means++ extraction plus Bradford chromatic adaptation for accuracy. Returns up to 5 dominant colours, each with archive name, cultural story, nearest RAL standard, and WCAG accessibility data. Works for product photography, interior photos, artwork, brand assets, and mood boards. The image is never stored — processed in memory only.

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• get_job_status

  DocImprint

  Poll the status of an async job (extract, indexing, batch). Free — no credits consumed. Use after add_document_to_collection or async extract to check when processing completes. Poll this endpoint in a loop until status is "complete" or "failed". Completed jobs include the bundle_id or result_json in the response. Jobs are created when you POST /v1/extract with a webhook, or when add_document_to_collection triggers async indexing. Returns: { id, type: "extract"|"extract_batch"|"index_collection", status: "queued"|"processing"|"complete"|"failed"|"cancelled", progress_pct: number (0–100), progress_message, bundle_id (when complete), result_json (when complete), error (when failed), created_at, completed_at } Example prompts: - "Check the status of my indexing job job_550e8400." - "Is my async extract job done yet?" - "Poll job [job_id] — what is the current progress?"

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• parse_invoice

  api

  Parse a receipt or invoice document into structured fields. Uses a quality AI model for accuracy. Use when you need to extract line items, totals, and merchant info from financial documents. For general document text, use extract_text instead. Returns: { invoice: { merchant, date (YYYY-MM-DD), line_items[], subtotal, tax, total }, cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Parse this invoice and give me the line items and total." - "Extract the merchant, date, and amounts from this receipt." - "Read this scanned invoice and return structured data."

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• download_resource

  Senzing

  Download workflow resources by name. Pass `filename` (string) or `filenames` (array); calling with neither returns the list of available resources (it does not fail). Available: sz_json_analyzer.py, sz_schema_generator.py, sz_verbatim_check.py, sz_routing_report.py, senzing_entity_specification.md, senzing_mapping_examples.md, identifier_crosswalk.json HTTP mode returns URLs; stdio mode returns `sz-mcp-coworker extract` commands. Supports batch via `filenames` array. Asset IDs are not stable across versions. If a previously-known ID fails to extract, call this tool again to obtain the current ID.

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• book_demo

  gethal.ai

  Submits a demo request. The prospect receives a confirmation email and must click the link in it before the request reaches a human at A Cloud Frontier. Use only when a real person has explicitly asked for a demo and provided their own working email address. Do NOT call this for testing, evaluation, or crawling purposes — automated and unconfirmable requests are rejected.

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• extract

  Sofya

  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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• image_palette

  Colour Memory

  Upload an image (base64 encoded) and extract its dominant colour palette, with each colour matched to its nearest named archive entry with full cultural provenance. Uses K-means++ extraction plus Bradford chromatic adaptation for accuracy. Returns up to 5 dominant colours, each with archive name, cultural story, nearest RAL standard, and WCAG accessibility data. Works for product photography, interior photos, artwork, brand assets, and mood boards. The image is never stored — processed in memory only.

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• extract_tables

  DocImprint

  Extract tables and forms as Markdown from a PDF or image (base64-encoded). Use when the document contains structured tabular data such as financial statements, data sheets, or forms. For plain prose documents, use extract_text instead. Returns: { pages: number, text: string } — text contains Markdown-formatted tables. Example prompts: - "Extract the tables from this financial statement." - "Pull the data table from this PDF into Markdown format." - "Get the tabular data from this form document."

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• extract_structured

  api

  Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, parse_invoice (prebuilt schema) may be simpler. For general summarization, use summarize_document instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."

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• metadata

  Fast.io

  AI metadata templates & extraction (the unstructured-data automation pipeline): template CRUD/clone, assign/resolve, the AI pipeline (eligible -> preview-match -> suggest-fields -> template-create -> nodes-add/-list -> auto-match -> extract-all), saved views, and lexical metadata search. Call action='describe' for the full action/param reference. Node-level metadata (get/set/delete/extract on a single file) lives on the `storage` tool. Destructive: template-delete, view-delete, nodes-remove. AI/credit side-effects: preview-match, suggest-fields, auto-match, extract-all (each spends AI credits).

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• politics_methodology

  officials

  Get the full transparency methodology — how we collect, assess, extract, sign, and verify political data. Includes quality assessment algorithm (signals + weights), trust levels, data sources, cron schedule, and what we can and cannot prove. Open and auditable.

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• get_sample_data

  Senzing

  Get real sample data for entity resolution. Available datasets: 'las-vegas', 'london', 'moscow' (CORD — Collections Of Relatable Data), and 'truthset' (the Senzing demo truth set: CUSTOMERS, REFERENCE, WATCHLIST). Use dataset='list' to discover datasets, source='list' to see the sources/vendors within a dataset. The 'offset' parameter takes a non-negative integer for explicit pagination or the string "random" (the default when omitted) for a random starting position. IMPORTANT: This is REAL data (not synthetic) — historical snapshots for evaluation only, not operational use. Always inform the user of this. When records are returned, a 'download_url' in the citation provides a way to fetch the full dataset. In HTTP mode this is a URL the user (or an automation) can curl; in stdio mode it is a `sz-mcp-coworker extract` command the user runs locally to pull bytes from the embedded bundle. Always present the fetch instruction to the user. Do NOT download it yourself or dump raw records into the conversation — the inline records are a small preview of the data shape. Asset IDs are not stable across versions. If a previously-known ID fails to extract, call this tool again to obtain the current ID.

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• enrich_html

  shopgraph

  Extract product data from raw HTML you already have (no HTTP fetch needed). Ideal when using Bright Data, Firecrawl, or any scraping API — pipe the HTML through ShopGraph for structured product data. Uses schema.org + LLM fallback. Costs $0.02 per call (cached results are free). Each field carries verification metadata in _shopgraph: provenance (field_method — which source/tier produced it: schema_org, llm, or hybrid), freshness (field_freshness — recency + volatility_class, for volatile fields like price & availability), and abstain (a field is null when ShopGraph cannot verify it on the page). Rely on provenance, freshness, and the abstain signal to decide what to trust.

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• framefetch_extract

  framefetch

  Extract data from ONE public social-video URL (YouTube incl. Shorts, TikTok, Instagram Reels, Pinterest, Reddit): metadata/insights/transcript/frames/digest/comments/etc — see `fields`. When NOT to use: non-video pages, private/login-walled content, or bulk crawling (one URL per call). Returns one JSON object with only the requested fields + a `cost` block (micro-USD); shapes: https://framefetch.net/docs. Cost scales with what you request (frames/transcript cost more than metadata). No key? POST /v1/keys {email} -> instant key (~100 free calls); or x402 (USDC), no account. Example: {"url":"https://www.youtube.com/watch?v=...","fields":["metadata","transcript"]}.

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• extract_page_markdown

  site-audit

  FREE PREVIEW (1 request per day per IP, quota shared with audit_website_preview) of Santos Page-to-Markdown extraction. Fetches one public page and returns its main content as clean Markdown plus title, description, outbound links, and word count. Single page only — no crawling or JavaScript rendering. For unlimited extraction, use the machine-payable production endpoint: POST https://api.santosautomation.com/v1/extract with {"url": "…"} — $0.005 USDC per successful extraction on Base mainnet (eip155:8453) via x402 v2 (or USD-equivalent XRP on XRPL when enabled); no account or API key required.

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• find_similar_sites

  mcp

  Find similar or competitor websites based on classification. Takes a URL, classifies it (or uses cached classification), and returns other websites from the same category and subcategory. Useful for competitive analysis and discovering related content. Rate limited to 1 request per minute per domain. Args: url: The website URL to find similar sites for. limit: Maximum number of similar sites to return (1-50, default 10). Returns: Dictionary with: - url: The input URL (normalized) - classification: The URL's category and subcategory - similar_sites: List of similar URLs from the same category - total_in_category: Total sites in this category/subcategory - cached: Whether the classification was from cache

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• get_metadata

  microlink

  Extract metadata from any URL to preview page content. Returns title, description, image, author, publisher, logo, and structured data—useful when you need to understand a webpage without visiting it directly.

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• extract_to_markdown

  Frenchie

  Extract structured documents (.docx, .xlsx, .csv, .tsv, .pptx) into Markdown through Frenchie. stdio mode auto-saves the result to .frenchie/<name>/result.md; HTTP mode returns inline Markdown.

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