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524,660 tools. Updated 2026-09-06 17:02

"A server for finding PDF documents" matching MCP tools:

  • Run an Australian identity check over a SET of identity documents. A vision model reads each document (which ID it is, which fields it shows — name/photo/address/signature — and its issue date); a deterministic engine then tallies them against a scheme and reports whether identity is established, and exactly what's still missing if not. USE THIS WHEN someone needs to verify a person's identity from their documents — KYC / onboarding / "do these documents satisfy the 100-point check?" Pass ALL the person's documents together (a passport alone is 70 points; the check needs >= 100). `documents` is a list, each item ONE of: {"url": "https://..."} (public link, fetched server-side) or {"bytes_b64": "...", "filename": "passport.pdf"} (inline). Up to 10. `scheme`: "afp_100_point" (points, default) or "austrac_safe_harbour" (category combinations). Returns `{established, points/target or satisfied_path, documents[] (per-document: type, fields shown, whether it counted and why-not), reason, accepts, ...}`. This is identity COVERAGE, not a forgery judgment — run verify_document for authenticity. Documents are never stored. Costs 2 credit(s) per call.
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  • Check whether a SET of documents satisfies a checklist — completeness, cheaply. USE THIS WHEN you have an application / onboarding pack and need "do we have the required documents, and what's still missing?" Each document is CLASSIFIED (one cheap page-1 read — never full field extraction or multi-page), then matched against the checklist's required slots. (For "is a document genuine?" use verify_document; to identify ONE document use extract_fields with options={"classify": true}; for the identity gate use verify_identity.) Define the checklist ONE of two ways: - `scheme`: a named preset — "income_proof", "lending_prequal", "rental_application". - `requirements`: an ad-hoc checklist — a list of document-type names like ["payslip","bank_statement"], or objects {"key":..., "accepts":[types], "optional":bool}. `documents` is a list (up to 12), each ONE of: {"url": "https://..."} (public link, fetched server-side) or {"bytes_b64": "...", "filename": "statement.pdf"} (inline). Returns `{complete, slots[] (key, satisfied, matched), missing[], documents[] (filename, classified_type), unmatched_documents[]}`. COVERAGE, not approval — that the right document TYPES are present, NOT that any is genuine (run verify_document) or that an application is approved. Documents are never stored. Costs 3 credit(s) per call.
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  • Analyzes Florida real estate title documents and returns a structured risk report: a risk score (0-100, higher is safer) and level, findings with verbatim evidence from the documents and guidance on how to cure each one, and the Schedule B-I requirements extracted from any title commitment in the package. Accepts one or more PDF documents as base64 strings (deed, title commitment, mortgage, closing disclosure, survey, payoff letter, HOA estoppel, etc.), including a single PDF containing a whole closing package, which is split into its constituent instruments. Submitting several documents together also enables cross-document checks for contradictions in parcel ID, address, and party names. Uses one of your 3 free analyses (sandbox tier) or 1 credit (paid tiers). Florida properties only — call check_coverage first to confirm scope, and get_credit_balance to confirm available credits.
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  • List canvas documents in a workflow run. Canvas documents are collaborative markdown files that multiple agents can edit in parallel. Omit run_id to list documents across all runs. Read-only. Use read_canvas for content and get_canvas_toc for section IDs. There is no get_run; list_runs returns run records. Pass playbook_id as the UUID or GUID of the playbook this call should target.
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  • 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.
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  • Upload and normalize a FINISHED, ready-to-mail document to PDF. Choose this when the content is final and IDENTICAL for every recipient — including when you mail the same letter to many people (just quote/pay once per recipient with the same documentId). The exact bytes you give are what gets printed. Use create_template instead only when the content must vary per recipient via {{fields}}. Returns a documentId, the stored page count, byte size, and source format. Free; no payment required. Provide the document EXACTLY ONE way: `content` (inline text, for html/markdown/text), `contentBase64` (base64-encoded binary, for pdf/docx/image), or `url` (a publicly reachable URL the server fetches). Supplying none, or more than one, is an error. Maximum upload size is 31457280 bytes (~30 MB); output page size is US Letter. Any `{{...}}` text is printed LITERALLY here — it is NOT treated as a merge field. If you want personalized mail merge across recipients, use `create_template` instead. Reserved address zone: a recipient address block is printed over the top ~3 inches of page 1, so the server reserves that space for you automatically. For text/html/markdown/docx, page-1 content is pushed below the block (content may therefore flow onto an additional page); for pdf and image inputs, a blank first page is prepended. As a result the returned page count — and the selected-provider cost behind the resulting quote — can be higher than your source document (e.g. a single-page PDF is stored as 2 pages). You do NOT need to leave the top of your document blank yourself. See the postagent://formats resource for per-format details.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    A production-ready Model Context Protocol server that bridges local document management with cloud synchronization (Notion) for AI agent integration, enabling seamless access and sync of local and cloud documents.
    15
    MIT

Matching MCP Connectors

  • High-fidelity PDF to structured Markdown conversion and document field extraction.

  • Send transactional pdfs for AI agents via SMTP. Templates included.

  • Build a complete creative intelligence profile from internal brand documents — creative briefs, brand guidelines, product specs, customer research, competitive analysis. Takes any mix of file_ids (from a previous upload), document_urls (public PDF/DOCX/TXT/MD links, up to 10), or documents_inline (base64-encoded files with filename), plus an optional context_url for layering live brand context (colors, fonts, current messaging) and optional idempotency_key. Returns a job_id; poll with get_powersource. Output shape is identical to create_powersource_url: identity, offer, selling points, voice, buyer profile, tensions, angles, emotional arcs, ctas, narrative. Use this when the user says "I have a brief", "here's my brand guidelines", "use this document", drops a PDF / DOCX / strategy deck, or when the truth lives in internal materials rather than the public website. The pipeline reads text only — convert PDFs to markdown before submitting via documents_inline when possible. Costs 100 credits. Do NOT use for URL-only scans — use create_powersource_url. For URL + docs combined (highest fidelity, triangulates public messaging against internal strategy), use create_powersource_full.
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  • Upload and normalize a FINISHED, ready-to-mail document to PDF. Choose this when the content is final and IDENTICAL for every recipient — including when you mail the same letter to many people (just quote/pay once per recipient with the same documentId). The exact bytes you give are what gets printed. Use create_template instead only when the content must vary per recipient via {{fields}}. Returns a documentId, the stored page count, byte size, and source format. Free; no payment required. Provide the document EXACTLY ONE way: `content` (inline text, for html/markdown/text), `contentBase64` (base64-encoded binary, for pdf/docx/image), or `url` (a publicly reachable URL the server fetches). Supplying none, or more than one, is an error. Maximum upload size is 31457280 bytes (~30 MB); output page size is US Letter. Any `{{...}}` text is printed LITERALLY here — it is NOT treated as a merge field. If you want personalized mail merge across recipients, use `create_template` instead. Reserved address zone: a recipient address block is printed over the top ~3 inches of page 1, so the server reserves that space for you automatically. For text/html/markdown/docx, page-1 content is pushed below the block (content may therefore flow onto an additional page); for pdf and image inputs, a blank first page is prepended. As a result the returned page count — and the selected-provider cost behind the resulting quote — can be higher than your source document (e.g. a single-page PDF is stored as 2 pages). You do NOT need to leave the top of your document blank yourself. See the postagent://formats resource for per-format details.
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  • Convert HTML or Markdown to a pixel-perfect PDF. Returns JSON: { url } — a temporary download URL (valid ~1 hour). Great for generating invoices, reports, receipts, or formatted documents programmatically. Supports full HTML/CSS including tables, images (base64 or URL), and inline styles. For Markdown input, set format='markdown'. 50 sats per conversion. Use convert_file instead for converting existing files between formats (e.g., DOCX→PDF). Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='convert_html_to_pdf'.
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  • Render a Markdown resume to a finished PDF using a ResumeMD template (default: classic — see list_resume_templates for all 32 ids). Returns JSON with a base64-encoded PDF and a suggested filename; decode the base64 to give the user the file. The PDF is generated in memory and never stored. For interactive editing, template switching, and color choices, send the user to resumemd.pro/editor instead.
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  • Read a document (PDF or image) from a URL and return its contents as markdown (tables preserved) or plain text. Costs $0.00075 per page, billed to the PennyOCR account; the response includes the exact cost_usd and per-page citations. Use estimate_cost first for big documents. Supports page ranges and hard spend caps.
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  • No arguments. Returns how many MCP servers have been read at source level, and the share of them with each category of finding (credential access, network egress, install-time execution, prompt-injection surface). Use this to judge whether checking a specific server is worth it before you look one up. It reports aggregate counts only - no per-server findings, and no verdict about any individual server.
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  • Converts a document to markdown or plain text: pass a public URL or the file itself as base64, and get back the content with headings, tables and lists preserved, at a fraction of the tokens that rendered pages cost. Use it when a harness has no native reader for the format — .docx, .xlsx, .odt and .numbers rarely have one — when a document is only a URL away, or when a long PDF's text matters and its layout does not. Handles PDF (.pdf), Word (.docx), Excel (.xlsx, .xlsm, .xlsb, .xls), OpenDocument (.odt, .ods), Apple Numbers, CSV, HTML, XML, and plain-text formats such as .txt and .md. The format is detected from magic bytes, not trusted from the file name, so a PDF served from a .php URL still converts. Two honest limits: a scanned PDF with no text layer has nothing to extract (this is conversion, not OCR), and legacy binary .doc and .ppt files are not readable — resave them as .docx or .pptx. Images are refused rather than described. Documents up to 10 MB.
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  • Brings a PDF into the connected asksteps account and stores it as a form template, so the answers people give can later be written back into that exact document. Requires "pdf:write". Unlike asksteps_analyze_pdf this one KEEPS the file, counts against the account's PDF-form quota, and also handles scanned documents through text recognition. It does NOT create the form: which fields become questions is the user's decision. You get a link that resumes the import in the asksteps studio with this template — no second upload. Pass exactly one of pdf_url or pdf_base64.
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  • Convert a document inline — pass the content directly as a string (or base64 for binary inputs like .docx). PREFERRED route for documents, and the one to use in sandboxed agent environments (claude.ai, Claude Desktop, Cursor): it runs entirely server-side, so it never needs the S3 upload those sandboxes block. Limit: up to 4 MB of content — already huge (a 500-page book is ~1 MB of text). For anything larger, use convert_from_url with a public URL. Supported inputs: md, html, rst, txt (plain text), docx (base64). Supported outputs: docx (Word), pdf, html, txt, md, rst, xlsx. Returns a job_id — poll get_job_status until 'complete', then get_output_content (inline bytes, sandbox-safe) or get_download_url (S3 link). Flat fee $0.05 per file. TIP: if you have shell access and are NOT sandboxed (e.g. a local coding agent), the `botverse` CLI (`npx botverse convert <file> --to <fmt>`) is faster for local files — it streams from disk instead of re-emitting the content through the model.
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  • Parse one supported document into markdown, HTML, links, summary, targeted answers, or JSON matching a schema. Supported inputs include common HTML, PDF, Word, RTF, OpenDocument, and spreadsheet files; PDF parsing can be bounded with `pdfOptions.maxPages`. Local MCP reads `filePath` from the server filesystem. Hosted MCP uses two calls: first provide `filePath` to receive upload instructions, upload locally, then call again with the returned `uploadRef`; do not send both fields together. Remote web URLs belong in `firecrawl_scrape`. Set `redactPII` to request redaction of personally identifiable information in the returned content. `zeroDataRetention` requires an eligible authenticated account; omit it for anonymous keyless use. Returns upload instructions for hosted phase one or parsed document content for the final call.
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  • Convert any document to another format without storing a template. Supports 100+ input/output format combinations: Office documents, PDFs, images, web pages, spreadsheets, and more. The source file can be a local path, a URL, or a base64 string. Carbone tags are PRESERVED, not resolved: converting a template keeps every {d.field} intact, so this is also how you proof a template in another format (DOCX template → PDF, or DOCX → ODT while it stays a template). Use render_document instead when you need data injection ({d.field} tags resolved), translations, or batch generation. Common conversions: DOCX → PDF (file: "report.docx", convertTo: "pdf"; add converter: "I" for the fastest DOCX→PDF path), XLSX → PDF (file: "data.xlsx", convertTo: "pdf"), PPTX → PDF (file: "slides.pptx", convertTo: "pdf", converter: "O" for best fidelity), HTML → PDF (file: "page.html", convertTo: "pdf", converter: "C" for full CSS/JS rendering), DOCX → HTML (file: "doc.docx", convertTo: "html"), XLSX → CSV (file: "sheet.xlsx", convertTo: "csv"), PDF → PNG (file: "doc.pdf", convertTo: "png"), PPTX → PNG (first slide as image), MD → PDF (file: "readme.md", convertTo: "pdf").
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  • Upload and normalize a FINISHED, ready-to-mail document to PDF. Choose this when the content is final and IDENTICAL for every recipient — including when you mail the same letter to many people (just quote/pay once per recipient with the same documentId). The exact bytes you give are what gets printed. Use create_template instead only when the content must vary per recipient via {{fields}}. Returns a documentId, the stored page count, byte size, and source format. Free; no payment required. Provide the document EXACTLY ONE way: `content` (inline text, for html/markdown/text), `contentBase64` (base64-encoded binary, for pdf/docx/image), or `url` (a publicly reachable URL the server fetches). Supplying none, or more than one, is an error. Maximum upload size is 31457280 bytes (~30 MB); output page size is US Letter. Any `{{...}}` text is printed LITERALLY here — it is NOT treated as a merge field. If you want personalized mail merge across recipients, use `create_template` instead. Reserved address zone: a recipient address block is printed over the top ~3 inches of page 1, so the server reserves that space for you automatically. For text/html/markdown/docx, page-1 content is pushed below the block (content may therefore flow onto an additional page); for pdf and image inputs, a blank first page is prepended. As a result the returned page count — and the selected-provider cost behind the resulting quote — can be higher than your source document (e.g. a single-page PDF is stored as 2 pages). You do NOT need to leave the top of your document blank yourself. See the postagent://formats resource for per-format details.
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  • Enumerate vault documents without FTS — for browsing by type or paginating. Requires Authorization: Bearer <edit token or friend token>. Friend tokens see only their scoped doc_types; access:private is invisible. Args: {doc_type?: string filter (note/pdf/literatura/calendar_event/...), limit?: int 1-200 default 50, offset?: int default 0}. Returns [{slug, title, doc_type, created_at, chunk_count}]. Vault offline ⇒ {status:offline}.
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  • Convert HTML and CSS to a PDF document using the WeasyPrint rendering engine. Supports every PDF/A archival level, PDF/UA accessibility and the PDF/X print standards. Best for professional documents: invoices, reports, certificates, contracts, and accessible documents. Also produces **fillable PDF forms** — set pdfForms to true. Send a complete HTML document including <html>, <head> with <style>, and <body> tags. Page geometry comes from the document's own CSS @page rule unless paperSize or orientation is set explicitly. Returns a temporary download URL for the generated PDF (valid for 30 minutes). Requires a paid PdfBroker.io plan (Starter or above). EU-first defaults: A4 paper, Portrait orientation when neither the document nor the caller says otherwise.
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  • PDF to Text — COPY THE WORDS OUT of a PDF: get the wording, sentences and paragraphs as plain text you can paste into an email, a document or a spreadsheet. Extract the text that is already inside a PDF and return it as a plain .txt file. Reads the PDF's existing text layer using pdftotext with a Ghostscript txtwrite fallback — it does NOT run OCR. A scanned or photographed document has no text layer, so this tool refuses it with a 422 naming pdf_ocr rather than returning an empty file; run pdf_ocr first to add a searchable text layer, then extract. Mixed documents still succeed: pages that yielded no text are reported in the X-Conversion-Notes response header instead of being dropped silently. [category: pdf]
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