"A tool for uploading and analyzing documents, extracting text from PDFs, and conducting research" matching MCP tools:

• word_createA

  Local MCPB

  Creates a new Word (.docx) document at `path` with the given text content (and an optional title rendered as the heading). The path must be somewhere LMCP can write; Desktop/Documents/Downloads may need a one-time Files-and-Folders grant (System Settings → Privacy & Security → Files and Folders). Returns {created, path}. For a OneDrive or Google Drive path use onedrive_write_file / gdrive_write_file; to append to an existing doc use word_append, to read one word_read.

  Connector

  Destructive

  OAuth

• searchA

  UseMyContext.aiA

  Search the user's files by filename and return matching documents in the deep-research result shape. ALIAS: this is the SAME search as search_files (same data, same permissions) - use it when your client requires the id/title/url search contract (ChatGPT deep research); otherwise prefer search_files for richer file metadata. Each result's id can be passed to fetch (or get_file) to read that document. Read-only; nothing is written, so it is safe to call.

  ConnectorNo auth

• send_faxA

  SendThisFaxA

  Submit an uploaded PDF for faxing. Step 1 (before this tool): upload the PDF over plain HTTP multipart, using any HTTP client you have — shell, JavaScript fetch with FormData, Python, etc.: curl -F "file=@document.pdf" https://www.sendthisfax.com/api/upload fetch("https://www.sendthisfax.com/api/upload", {method: "POST", body: formDataWithFile}) The response contains fax_public_id and page_count. PDFs must be unencrypted, at most 50 MB and 1000 pages. Step 2: call this tool with the fax_public_id and the recipient fax number. Two modes: - With an API key (Authorization: Bearer stf_live_... on this MCP connection): the fax price is debited from the prepaid credit balance and sending starts immediately — no checkout, no browser. sender_email and billing_country are optional (they default to the key's records). Buy credits at https://www.sendthisfax.com/en/credits. - Without an API key: sender_email and billing_country are REQUIRED and the tool returns a checkout_url the USER must pay in a browser; the fax is sent automatically once paid. In both modes, poll get_fax_status until status reaches "delivered" or "failed" (failures after payment are auto-refunded). For integration testing, +19898989898 is the designated test recipient number.

  ConnectorNo auth

• scan_tool_descriptionA

  KamyA

  Heuristic pattern scan of MCP tool description text for prompt-injection tells — instructions addressed at the reading model, data-exfiltration hints, attempts to override your system prompt or hide content. Run it on descriptions from third-party MCP servers before you act on what they say. Returns risk 'low' | 'medium' | 'high' and the matched findings with excerpts. This is a heuristic aid, NOT a security boundary: a 'low' verdict is not evidence that a tool is safe, and an injection phrased to avoid the patterns will score low. Do not treat any result here as clearance to trust an untrusted tool — keep your own judgement and human review in the loop. Read-only: it analyses only the text you pass in and fetches nothing. Requires a Kamy API key.

  ConnectorNo auth

• scan_tool_descriptionA

  KamyA

  Heuristic pattern scan of MCP tool description text for prompt-injection tells — instructions addressed at the reading model, data-exfiltration hints, attempts to override your system prompt or hide content. Run it on descriptions from third-party MCP servers before you act on what they say. Returns risk 'low' | 'medium' | 'high' and the matched findings with excerpts. This is a heuristic aid, NOT a security boundary: a 'low' verdict is not evidence that a tool is safe, and an injection phrased to avoid the patterns will score low. Do not treat any result here as clearance to trust an untrusted tool — keep your own judgement and human review in the loop. Read-only: it analyses only the text you pass in and fetches nothing. Requires a Kamy API key.

  ConnectorNo auth

• read_primary_textA

  tw-market-dataA

  Read the FULL TEXT of filings and announcements — with proof links and a knowledge cutoff. ★ NOT `search_filings`. That one ranks passages by similarity and hands you fragments; this hands you whole documents so you can read what was actually said and where it sat in the filing. Similarity is not importance, and a fragment cannot show you its own context. ★ POINT-IN-TIME: pass `as_of` (YYYY-MM-DD). The cutoff is applied in SQL on the source's declared knowledge-time column BEFORE the row limit, so a bounded read is a true prefix of what was knowable, not a random subset of it. **Without `as_of` the read is NOT point-in-time** and says so in `warnings`. ★ NO SENTIMENT, NO SCORES — deliberately. Judging the text is your job. A stored score is one model's output on one day; after that model changes, the stale number still sits in the table looking exactly like a fresh one. ★ READ `corpus_reality` BEFORE CONCLUDING ANYTHING. The full-text corpus is SMALL and the response says how small. One source carries ~1M rows of TITLES ONLY — a large row count there is breadth, not depth, and "what did they say about it" is not answerable from titles. ★ A ticker that returns nothing appears in `coverage.missing`. That means nothing is held for it under those filters — NOT that the company disclosed nothing. Do not fill the gap. Args: source: which corpus, e.g. 'announcements_fulltext' or 'mops_major_event'. tickers: restrict to these codes, e.g. ['2330']. as_of: knowledge cutoff (YYYY-MM-DD). since: optional lower bound on the same knowledge-time column. limit: max documents (these are whole documents; keep it small).

  ConnectorNo auth

• convert_pdfA

  pdfintactA

  Convert a PDF into structured content (tables, charts, formulas, headings, body text) using a two-stage pipeline (layout detection, then a vision-language model) rather than a single VLM call on the raw PDF -- calling a VLM on a raw PDF directly is a known-unreliable pattern for numeric tables. Measured accuracy (500-page real-world benchmark of government/corporate reports, ~51,000 table values checked): tables 95.2% digit-exact, body text 88.8%. This tool reads PDFs a VLM cannot read directly, including scanned pages and PDFs with corrupted/garbled text layers (common in older Japanese academic PDFs). For scanned Japanese documents the numbers hold up (99.4% on the same benchmark). For scanned Arabic, body text does NOT: characters are dropped mid-sentence and quantities can turn into different quantities, so body blocks from scanned Arabic are always flagged confidence:"estimated" -- tables in the same documents stayed exact in our measurement. Strong on Japanese-language documents specifically; the accuracy figures above were measured on Japanese material and are not a claim about every language. Chart values are extracted but are best-effort estimates (about 52% exact match, excluding axis tick labels) and are always flagged confidence:"estimated" in the result -- do not treat estimated chart numbers as authoritative. This is a PAID, ASYNCHRONOUS, per-page-billed operation: credits are reserved from the caller's PDFIntact balance before processing starts, and the response's _meta.credits_remaining shows the balance right after reservation. Processing takes real wall-clock time (roughly 7 seconds/page; a 500-page PDF takes about 42 minutes including a multi-minute cold start), so this tool returns a job_handle immediately without waiting -- call get_result with that job_handle to poll for completion instead of calling convert_pdf again. Always pass idempotency_key; reuse the exact same value if you retry the same request, otherwise retries can double-charge and double-process. Provide the PDF either as a public https URL (source.type="url", up to ~200MB) or inline base64 (source.type="base64", up to ~20MB) -- prefer the URL form for large files. Requires sign-in (OAuth): this session is not authenticated, so calling this tool will fail until the PDFIntact account is connected and authorized.

  ConnectorNo auth

• document.reprocessA

  FlexOrch MCPA

  Re-queue an already-uploaded document through the pipeline. Use this to re-run extraction/PII detection/quality scoring on a document you've already processed — e.g. after a document_type_hint change, without downloading and re-uploading the original file. Returns immediately with a job_id — poll with job.status the same way as after document.process. Only works for documents whose original file is still stored on the server (locally uploaded, not connector-sourced). If the file is no longer available, re-upload it with document.process instead. Args: document_id: ID of a previously processed document. pipeline_config: Optional pipeline config overrides, e.g. {"document_type_hint": "invoice"}.

  ConnectorNo auth

• merge_pdfA

  Razi ToolsA

  Concatenate two or more PDFs into a single PDF, in the order supplied, and return the merged file. Over MCP the PDF is never inlined: it comes back as a stored URL that stays retrievable for about 24 hours. Page content is copied unchanged — it does not compress (use compress_pdf) or select pages (use split_pdf). Fewer than two files is rejected. Every input must already be a PDF; a photo handed to this tool is rejected rather than converted. Use images_to_pdf when any input is a picture — it takes PDFs alongside them and splices both in one pass. There is no upload channel over MCP: pass `fileUrls`, an array of URLs in razi.pro's own storage; third-party URLs are rejected. Obtain such a URL by uploading the file over the REST API first (POST /api/v1/tools/execute with the file attached). Over the REST API the files may instead be attached as multipart/form-data. Limited to 20 merges per hour per IP.

  ConnectorNo auth

• merge_pdfA

  Razi Document ToolsA

  Concatenate two or more PDFs into a single PDF, in the order supplied, and return the merged file. Over MCP the PDF is never inlined: it comes back as a stored URL that stays retrievable for about 24 hours. Page content is copied unchanged — it does not compress (use compress_pdf) or select pages (use split_pdf). Fewer than two files is rejected. Every input must already be a PDF; a photo handed to this tool is rejected rather than converted. Use images_to_pdf when any input is a picture — it takes PDFs alongside them and splices both in one pass. There is no upload channel over MCP: pass `fileUrls`, an array of URLs in razi.pro's own storage; third-party URLs are rejected. Obtain such a URL by uploading the file over the REST API first (POST /api/v1/tools/execute with the file attached). Over the REST API the files may instead be attached as multipart/form-data. Limited to 20 merges per hour per IP.

  ConnectorNo auth

• get_invoice_submissionsA

  StackinA

  Show every attempt made for one invoice and what came back. This is the tool that answers "why was it rejected". consult_invoice gives the status; this gives the tax authority's own code, its message, and the request and response exactly as they went over the wire. Read-only. It takes the invoice_id, like reissue_invoice — a rejected document has no access key to look it up by. Get the id from list_invoices. The rows are attempts, not documents: a reissued invoice has more than one, oldest first, and only the last describes the current state. Quote the authority's message rather than paraphrasing it; the code is what the user will search for.

  ConnectorNo auth

• check_textA

  WSC - Writing Style CheckerA

  Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.

  ConnectorNo auth

• getInstagramPostInteractingUsersA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Get USER PROFILES of people who interacted with an Instagram post. Returns full user data (bio, followerCount, followingCount, etc.). RETURNS USER PROFILES: id, username, fullName, biography, followerCount, followingCount, isVerified, profilePicUrl. Use for analyzing WHO engaged with a post. NOT FOR COMMENT TEXT: To read the actual comment content (what people wrote), use getInstagramCommentsByPostId instead. INTERACTION TYPES: "commenters" (users who commented), "likers" (users who liked). WHEN TO USE THIS TOOL: Analyzing commenters/likers demographics, finding influencers who engaged, building audience profiles, network analysis of who interacts with posts. WHEN TO USE getInstagramCommentsByPostId: Reading comment text, sentiment analysis of what was said, analyzing discussion content. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for commenters when stale. PAGING (responseType="paging"): Async paginated results (1000 users per page with default fields), returns operationId - IMMEDIATELY call checkOperationStatus to get results. CSV export included via dataDumpExportOperationId. Supports pageNumber/tableName for subsequent pages. Optional fields (default: ["id", "username", "fullName"]). Available: biography, isPrivate, isVerified, followerCount, followingCount, mediaCount, profilePicUrl. This is a safe, read-only tool for analyzing searchable information.

  ConnectorOAuth

• parse_pdf_to_textA

  Nordic Financial MCPA

  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.

  ConnectorNo auth

• extract_ibans_from_textA

  ibanchecker-mcpA

  Scan a free-form block of text and pull out every candidate IBAN, then validate each one. Useful for unstructured sources such as emails, invoices, PDFs pasted as text, or chat messages where IBANs appear inline and may be split by spaces or surrounded by other words. Returns JSON with `count`, `valid_count`, `invalid_count` and `results`, one `validate_iban`-shaped entry per IBAN found; text containing no IBAN returns an empty list rather than an error. Use this as the first step when the account number is buried in prose; pass the extracted IBANs to `validate_bulk_ibans` only if you need to re-check them separately. Input text is processed in memory and not stored. Requires an API key on the Growth plan or above; a free key whose address has a verified ibanchecker.cash account can try it with up to 5,000 characters per call. Each IBAN found counts as one request.

  ConnectorNo auth

• get_job_statusA

  CatchAll (by NewsCatcher)B

  Check the status of a submitted job. Call this after submit_query to see if your job is ready. Status progression: submitted -> analyzing -> fetching -> clustering -> enriching -> completed/failed IMPORTANT: Jobs take several minutes to process. First check after ~1-2 minutes, then poll every 30-60 seconds. Broad searches can take 10-30+ minutes; for long jobs, poll every 60-120 seconds. Do NOT call this tool in a tight loop. Stop polling when status is `completed` or `failed`. Treat `submitted`, `analyzing`, `fetching`, `clustering`, and `enriching` as active states and continue polling. You don't need to wait for completion to pull results. Partial results are available during `enriching` — call pull_results after ~2 minutes, then poll status every 30-60 seconds and pull again for fresher results. Do not stop pulling just because an intermediate pull is empty/unchanged. Use `progress_validated` vs `candidate_records` to track whether more results may still appear (`progress_validated < candidate_records`). If transport/session fails, resume using the same `job_id`.

  ConnectorNo auth

• check_textA

  Writing Style CheckerA

  Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.

  ConnectorNo auth

• get_job_statusA

  CatchAllB

  Check the status of a submitted job. Call this after submit_query to see if your job is ready. Status progression: submitted -> analyzing -> fetching -> clustering -> enriching -> completed/failed IMPORTANT: Jobs take several minutes to process. First check after ~1-2 minutes, then poll every 30-60 seconds. Broad searches can take 10-30+ minutes; for long jobs, poll every 60-120 seconds. Do NOT call this tool in a tight loop. Stop polling when status is `completed` or `failed`. Treat `submitted`, `analyzing`, `fetching`, `clustering`, and `enriching` as active states and continue polling. You don't need to wait for completion to pull results. Partial results are available during `enriching` — call pull_results after ~2 minutes, then poll status every 30-60 seconds and pull again for fresher results. Do not stop pulling just because an intermediate pull is empty/unchanged. Use `progress_validated` vs `candidate_records` to track whether more results may still appear (`progress_validated < candidate_records`). If transport/session fails, resume using the same `job_id`.

  ConnectorNo auth

• upload_pdfA

  KDAN PDFA

  Upload a PDF from a ChatGPT file attachment. MANDATORY WORKFLOW — follow EVERY step in order: 1. ALWAYS call check_upload_status FIRST — even if you think the file is new. 2. If a job with the same filename already exists, reuse its job_id — do NOT call upload_pdf. 3. Only call upload_pdf if the file is confirmed absent from check_upload_status. Skipping step 1 and calling upload_pdf directly is FORBIDDEN. Use this when the user provides a file attachment in ChatGPT. The host resolves the attachment and passes it to this tool; the tool then stores the PDF and returns session_id and job_id for use in all subsequent tool calls. Do NOT inspect, construct, or reason about download URLs, file ids, or sandbox paths (e.g. '/mnt/data/...') — just pass the attachment straight through. NEVER invent, guess, or synthesise a download_url or file_id. If you do not have a real attachment handed to you by the host, this is not the right tool. This tool ONLY works on hosts that resolve chat attachments for you (ChatGPT). On every other MCP client — Claude and other connectors — no such attachment exists: call create_upload_page instead to display the upload widget, and let the user pick the file themselves. Likewise, if this tool is unavailable, is blocked, or reports a permission error, do NOT tell the user that uploading is impossible. Fall back to create_upload_page.

  ConnectorNo auth

• create_upload_pageA

  KDAN PDFA

  Display an interactive PDF upload widget directly in the chat. Use this when the user wants to upload a local PDF file from their device. This is the standard upload method for MCP clients (e.g. Claude) where file attachments with download URLs are not available. Do NOT call upload_pdf when using this tool — the widget handles the upload automatically. The widget renders inline and the PDF viewer appears after the user selects a file. Do NOT call view_pdf after this tool; the widget manages the UI. Never tell the user the file is still uploading; the widget handles the spinner. After the user uploads via the widget and notifies you, call check_upload_status(session_id=<session_id>) to discover the uploaded file and its job_id before proceeding with any operation.

  ConnectorNo auth

• searchA

  UneedA

  Use this when a deep research run needs to look up digital tools and products on uneed.best. Same catalog and same relevance ordering as search_products, returned as `{id, title, url}` documents; pass a result's id to `fetch` for the full profile. Prefer search_products when you want structured product fields directly.

  ConnectorNo auth