"Methods to Extract Text Content from Videos" matching MCP tools:

• parse_pdf_to_text

  Nordic Financial MCP

  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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• url.extract

  DocImprint

  Fetch a public HTTPS URL and return extracted text and page metadata. Lean mode — no evidence bundle stored, no bundle_id returned. Use for raw text extraction from web pages and online documents. Use url.summarize for summaries, url.qa for Q&A, url.translate for translation, document.extract_text for base64 file uploads. Returns: { url, title, word_count, text, final_url (after redirects) } Example prompts: - "Extract the text from https://example.com/report.pdf for me." - "Get me the raw content of this web page: [URL]." - "Pull the text from this online article so I can analyze it."

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• url.extract

  api

  Fetch a public HTTPS URL and return extracted text and page metadata. Lean mode — no evidence bundle stored, no bundle_id returned. Use for raw text extraction from web pages and online documents. Use url.summarize for summaries, url.qa for Q&A, url.translate for translation, document.extract_text for base64 file uploads. Returns: { url, title, word_count, text, final_url (after redirects) } Example prompts: - "Extract the text from https://example.com/report.pdf for me." - "Get me the raw content of this web page: [URL]." - "Pull the text from this online article so I can analyze it."

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• analyze_video

  switch

  Switch Vision — watch and understand a video (or image) like a human and answer a question about it: scenes, subjects, actions, on-screen text, pacing, mood and sentiment. Pass video_url (a public https video URL, including YouTube) OR one of your own Switch videos (a video/asset id from list_my_videos / list_my_assets / upload_media). Add an optional question to focus the analysis (e.g. "what is the tone and energy?", "list the cuts and what each shot shows"). Use this whenever the user gives you a reference video and wants its style, energy, structure or content understood — for example before making a new video that matches it.

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• web_url_reader

  Inferventis MCP Server

  Fetches any public web page and returns clean, readable plain text stripped of HTML, navigation, scripts, advertisements, and boilerplate. Returns the page title, meta description, word count, and main body text ready for analysis or summarisation. Use this tool when an agent needs to read the content of a specific web page or article URL — for example to summarise an article, extract facts from a page, verify a claim by reading the source, or convert a web page into plain text to pass to another tool. Pass article URLs returned by web_news_headlines to this tool to read full article content. Do not use this tool to discover current news headlines — use web_news_headlines instead. Does not execute JavaScript — best suited for standard HTML content pages. Will not work with paywalled, login-protected, or JavaScript-rendered single-page applications.

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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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• chrome_read_tab

  local-mcp

  Reads the rendered text content of a Google Chrome tab. Call with NO arguments to read the current (active) tab — the same tab chrome_wait_for/chrome_click/chrome_navigate just operated on; use this right after chrome_wait_for, since the tab may have navigated and its URL changed. Or identify a specific tab by `url_match` (substring match against URL; first hit wins — falls back to the active tab if nothing matches) or by `window_index` + `tab_index` (from chrome_list_tabs). Text is capped at `max_bytes` (default 100 KB). Pass `include_html: true` to also get the raw HTML source. Pass `include_links: true` to extract all links with their href and text. Requires 'Allow JavaScript from Apple Events' (Chrome → View → Developer); run chrome_setup_check if reads come back empty.

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• document.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, document.parse_invoice (prebuilt schema) may be simpler. For general summarization, use document.summarize 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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• rendex_extract

  Rendex: Rendering API for Images, PDFs & Content Extraction

  Use this when the user asks to read, extract, get the text/content/article of, or summarize a webpage/URL. Do NOT use for a visual screenshot (use rendex_screenshot). Extracts clean reader-mode content from any webpage as Markdown, JSON, or HTML. Runs the same Chromium render pass as a screenshot, so it captures content after JavaScript runs — handles SPAs that fetch-only readers miss. Strips nav, ads, and boilerplate, returning the article body plus title, byline, and excerpt. Great for feeding page content to an LLM, summarization, or RAG ingestion. Costs 1 render credit per call.

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• niche_voice_profile_ingest

  Niche — Editorial Intelligence

  Extract voice primitives (register / sentence rhythm / lexicon preferences / punctuation habits) from post-shaped text and persist onto the user's VoiceProfile. The voice primitives thread into content generation so generated copy matches the user's actual writing voice. Two input shapes: pass `posts` (list of pre-collected text snippets, ≥80 chars each) or pass `url` (the server scrapes post-shaped snippets from the page: Substack / Medium / blog / X profile). Inline posts win when both are given. Inline post-shaped snippets need to be the user's own writing, not press articles or marketing copy. Returns the extracted primitives + a diff of what changed on the stored VoiceProfile.

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• x711_data_retrieval

  x711

  Fetches clean text from any public HTTPS URL. Use x711_web_search first to find the URL, then this tool to read it. Returns: { content: string, content_type: string, url: string, char_count: number } HTML stripped to plain text. JSON returned as-is. Blocked: localhost, private IPs, .internal domains.

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• get_full_text

  Pubmed

  Fetch the FULL TEXT of a biomedical paper from PubMed Central (the open-access subset) by PubMed ID. PREFER OVER get_abstract when you need methods/results/discussion, not just the abstract — "read the full paper", "what methods did <PMID> use", "extract details from the paper". Resolves the PMID to its PMC id and returns the article body text (capped ~40k chars). Only open-access articles are in PMC — returns has_full_text:false (use get_abstract) otherwise.

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• x711_data_retrieval

  x711 — Universal Agent Gas Station

  Fetches clean text from any public HTTPS URL. Use x711_web_search first to find the URL, then this tool to read it. Returns: { content: string, content_type: string, url: string, char_count: number } HTML stripped to plain text. JSON returned as-is. Blocked: localhost, private IPs, .internal domains.

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• document.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, document.parse_invoice (prebuilt schema) may be simpler. For general summarization, use document.summarize 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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• document.extract_text

  api

  Extract plain text from a PDF or image (base64-encoded). Use when you need raw text for downstream AI analysis (summarization, claim checking, structured extraction). For documents at a public URL, use url.extract instead (no base64 encoding needed). Returns: { pages: number, text: string } Example prompts: - "Extract the text from this scanned contract so I can search it." - "Give me the raw text from this PDF document." - "OCR this image and return the text content."

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• document.extract_tables

  api

  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 document.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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• parse_address

  Qiniso

  USE THIS to extract structured {country, postcode, city, state} from a free-text UK or US address — when onboarding a user, running a KYC/fraud check, or storing an address — instead of splitting the string yourself. Returns a confidence flag.

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• create_content_campaign

  ProductClank

  Launch a content campaign: rally the ProductClank community to create content (posts, threads, videos) for a product. Spends 1000 credits. The platform's AI expands your brief into a full campaign and auto-activates it; community submissions and winner selection happen in the ProductClank web app. Requires a product_id from search_products. Preview with suggest_content_campaign and confirm the 1000-credit cost with the user before calling.

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• fetch_structured

  Agent Rescue

  Fetch a public URL and return its content as JSON validated against the schema you pass. Use it when you need data from a page and cannot parse it reliably yourself. Do not use it for URLs requiring authentication or a session, for internal networks, or when you already have the content — extract it yourself in that case. `extracted_by` tells you where the answer came from: `structured_data` when the page already published it (JSON-LD, OpenGraph, or a JSON body — exact and free) or `model` when it had to be extracted from the text. If structured extraction is unavailable, the response carries `degraded: true`, `data: null` and the page `text` for you to parse: check `degraded` before reading `data`.

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• extract_tables

  AI NetCafé

  Extract tables from a PDF into structured rows (JSON + CSV). Pass fields to force a fixed set of columns — that aligns a pile of documents that each name their headers differently into one consistent table. Rows the model was unsure about are flagged rather than guessed. Text-layer PDFs only.

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