"A tool for converting PDF files to text format" matching MCP tools:

• detect_ai_text

  OpenWarrant — AI Text Detection

  Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose). USE THIS WHEN someone shares prose — an essay, cover letter, article, review, application, or report (or a link to one) — and asks: did an AI / ChatGPT write this? is this human-written? detect AI text. Provide the document ONE way: `text` (pasted markdown/plain prose), `url` (a public http(s) link to a page or PDF — fetched server-side, the cheapest call), OR `bytes_b64` (a base64 PDF/file, plus `filename` for routing). Returns `{probability, lean, tells, reasoning, applicable}`. HONEST SCOPE: the probability is the model's CONFIDENCE, not a calibrated truth — it can false-flag templated/coached or non-native-English writing. It works on PROSE only: for a form/table/numeric document (payslip, statement) it returns `applicable: false` and abstains, because AI-text detection false-positives badly there — use `verify_document` (the authenticity engine) for those, and `verify_references` to check a doc's citations/claims.

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• 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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• format_auto

  Caliper

  Auto-detect geometry file format and extract metadata statistics. Accepts a 3D geometry file via URL or base64 and returns structured metadata: bounding boxes, triangle counts, manifold analysis, point cloud statistics, and more. This is a read-only analysis tool — it does not perform mesh repair, format conversion, or boolean operations. Supported formats: STL, OBJ, PLY, PCD, LAS/LAZ, glTF/GLB. STEP and IGES support is planned. Provide either file_url (preferred for large files) or file_b64 (for files under 200KB). Include filename for format detection if using file_b64. When using file_url, the format is detected from the URL path extension; filename is not required. Files under 150KB are free. Larger files cost $0.02/MB via x402 (USDC on Base) or card via MPP (Stripe; adds $0.35 surcharge). If payment is required, the response includes payment details. Retry with the payment argument containing the payment proof. Privacy policy: https://caliper.fit/privacy

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• merge_pdfs

  Sats4AI - Bitcoin-Powered AI Tools

  Merge multiple PDF files into a single document. Preserves bookmarks, links, and formatting. Returns JSON: { url } — a temporary download URL (valid ~1 hour). Minimum 2 files, no maximum. Files are concatenated in array order. 100 sats per merge regardless of file count. Use convert_file instead if you need format conversion (e.g., DOCX→PDF). Pay per request with Bitcoin Lightning — no API key, no account needed. Requires create_payment with toolName='merge_pdfs'.

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• convert_html_to_pdf

  Sats4AI - Bitcoin-Powered AI Tools

  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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• get_doc

  Dock

  Read a workspace's doc (TipTap rich-text) body. Format is negotiable via `format`: `markdown` (default — CommonMark + GFM, ready to feed to an LLM or render in a non-ProseMirror surface), `content` (TipTap JSON, round-trippable into update_doc for structural edits), `text` (plain text, best for search, summarisation, word-count heuristics), or `all` for the legacy three-in-one shape. Default is `markdown` because it's the slice agents need 95% of the time and the JSON form on a long doc can blow past the agent harness's tool-result token cap. Pass `format: "content"` only when you're round-tripping into update_doc for a structural edit. A workspace can hold any combination of doc and table surfaces, one or many of either kind; omit `surface_slug` to read the primary doc surface, or pass it to target a specific doc tab (use `list_surfaces` to enumerate). An unwritten or absent doc returns the requested format empty (markdown="", content={}, text=""); a `surface_slug` that doesn't match any live doc surface 404s.

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• export_dataset

  DC Hub — Data Center & Energy Intelligence

  Use when a user wants to pull their saved DC Hub shortlist OUT of the platform for offline analysis, a spreadsheet, or ingestion into another tool (PRO). Example: "Export my saved sites as GeoJSON for QGIS." — export_dataset format=geojson. Params: format ("csv" default, or "geojson"). Returns: the full file contents as text — CSV rows or a GeoJSON FeatureCollection of your saved sites with DCPI score, target MW, market, coordinates, and notes. Do NOT use to list sites in-chat (use list_saved_sites) or to save a new one (use save_site); this is the bulk-download path.

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• files_read

  DialogBrain

  Read **text content** of an attached file. Works for: .txt, .md, .json, code files, and PDFs (after files.ingest extracts text). DO NOT call on binary files — for IMAGES use `files.get_base64`, for AUDIO/VIDEO it cannot be transcribed via this tool, and for non-PDF DOCUMENTS run `files.ingest` first, THEN files.read. Calling on a binary mime-type returns an error — saves you a turn to read the routing hint before deciding.

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• files_read

  dialogbrain

  Read **text content** of an attached file. Works for: .txt, .md, .json, code files, and PDFs (after files.ingest extracts text). DO NOT call on binary files — for IMAGES use `files.get_base64`, for AUDIO/VIDEO it cannot be transcribed via this tool, and for non-PDF DOCUMENTS run `files.ingest` first, THEN files.read. Calling on a binary mime-type returns an error — saves you a turn to read the routing hint before deciding.

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• convert_content

  Botverse

  Offload an inline document conversion to Botverse — pass the content directly as a string. ONLY use this tool for content you generated yourself (e.g. Markdown you just wrote). HARD LIMIT: content must be under 10,000 characters. If the content is longer than 10,000 characters, or came from an uploaded or external file, DO NOT use this tool — tell the user to make the file available at a public URL (Google Drive share link, Dropbox, S3, etc.) and use convert_from_url instead. Supported inputs: md, html, rst, txt (plain text), docx (base64). Supported outputs: docx (Word), pdf, html, txt, md, rst, xlsx. Flat fee $0.05 per file.

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• detect_ai_text

  OpenWarrant — Document Verification Suite

  Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose). USE THIS WHEN someone shares prose — an essay, cover letter, article, review, application, or report (or a link to one) — and asks: did an AI / ChatGPT write this? is this human-written? detect AI text. Provide the document ONE way: `text` (pasted markdown/plain prose), `url` (a public http(s) link to a page or PDF — fetched server-side, the cheapest call), OR `bytes_b64` (a base64 PDF/file, plus `filename` for routing). Returns `{probability, lean, tells, reasoning, applicable}`. HONEST SCOPE: the probability is the model's CONFIDENCE, not a calibrated truth — it can false-flag templated/coached or non-native-English writing. It works on PROSE only: for a form/table/numeric document (payslip, statement) it returns `applicable: false` and abstains, because AI-text detection false-positives badly there — use `verify_document` (the authenticity engine) for those, and `verify_references` to check a doc's citations/claims.

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• render_mermaid

  Diagrams MCP

  Render a Mermaid diagram definition and return the image with metadata. The definition should be valid Mermaid syntax (e.g. flowchart, sequence, class, ER, state, or Gantt diagram). Returns a list of content blocks: the rendered image plus a JSON text block with metadata including a mermaid.live edit link for opening the diagram in a browser editor. Args: definition: Mermaid diagram definition text. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.

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• upload_file

  BorealHost

  Upload a base64-encoded file to a site's container. Use this for binary files (images, archives, fonts, etc.). For text files, prefer write_file(). Requires: API key with write scope. Args: slug: Site identifier path: Relative path including filename (e.g. "images/logo.png") content_b64: Base64-encoded file content Returns: {"success": true, "path": "images/logo.png", "size": 45678} Errors: VALIDATION_ERROR: Invalid base64 encoding FORBIDDEN: Protected system path

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• get_filing_extract_meta

  ca-rate-filings

  Lists **what's in** each extracted artefact for a filing — section counts, item names, and the page each item came from — without returning any of the bulky factor tables, descriptions, or rate rows themselves. **Call this FIRST**, before `get_filing_extracts`, for any "what does this filing contain" question. It costs a fraction of the tokens and tells you which file + which section you need to pull in detail. `get_filing_extracts` is then the targeted second call once you know the SERFF + file + section that actually answer the user's question. Use this when the user asks: - "What forms does this filing include?" / "List the form numbers in TSIS-134726605." - "How many exclusions does it carry? What are they called?" - "What rate tables are in this filing, and which PDF page are they on?" - "List the discounts / endorsements / coverages this filing offers." - "Where in the source PDF is the territory rate table?" - Any "how many", "what are the names of", or "which page is X on" question about a filing's extracted artefacts. Wrong surface for: - Anything that needs the actual numeric content (factor values, full rate rows, full exclusion text). Call `get_filing_extracts` instead, narrowing `files` to just the one(s) you discovered here. Whitelist (same as `get_filing_extracts`): - `calculations.json` — example rate-calculation walk-throughs. - `coverages.json` — coverage definitions (perils, limits, applicability). - `deductibles.json` — deductible options + factors. - `discounts.json` — discount / surcharge schedules. - `endorsements.json` — optional endorsements / riders. - `examples.json` — worked policyholder rating examples. - `exclusions.json` — coverage exclusions + the conditions they apply to. - `extraction_summary.json` — structured filing-overview fields. - `final_rating_calculation.json` — canonical rating expression. - `forms.json` — policy form numbers + types. - `rates_data.json` — base rates + rate-table headers. - `underwriting_guidelines.json` — eligibility / UW rules. Per item the tool returns `{ name, source_page? }`. The item name is picked from whichever identifying field exists (`name` → `form_number` → `id` → `key` → `code` → `coverage` → `label` → `title`). `source_page` is the page in the source PDF where the item was extracted from, when the pipeline recorded one. `rates_data.json` items additionally carry `source_file` — the source PDF the rate table lives in — when the filing has a single source PDF. Multi-source filings get `source_file_note` flagging the limit (per-item `source_file` on non-rate extracts needs a pipeline-side change, deferred). Args: `serff` (required), `files` (optional — pass a subset of the whitelist to narrow; omit for all 12). Returns: `{ serff, files: { "<name>": { file_name, filing_ref?, confidence?, sections: { "<key>": { count, items: [...] } }, total_items } }, count, skipped }`.

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• bulk_schedule

  SendIt

  Schedule multiple posts at once from CSV content. USE THIS WHEN: • User has a spreadsheet or list of posts to schedule • Planning a content calendar for a month • Migrating content from another tool CSV FORMAT (required columns): • platform: linkedin, instagram, x, tiktok, threads • scheduled_time: ISO 8601 format (e.g., 2024-02-15T10:00:00Z) • text: Post content/caption OPTIONAL COLUMNS: • media_url: Image or video URL • first_comment: First comment to add (Instagram/LinkedIn) • hashtags: Additional hashtags to append PROCESS: 1. First call with validate_only: true to check for errors 2. Review validation report with user 3. Call again with validate_only: false to execute import

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• get_tafsir_text

  Tarteel MCP Server

  INTERNAL/preparatory tool — text-only, no widget rendered. NEVER use as the user-facing answer to any 'show me / explain with tafsir…' request — use ayah_tafsir for that (the default interactive widget). Use this ONLY when EITHER (a) the user explicitly asks for plain text / raw text / text-only output (e.g. 'give me just the commentary text', 'no widget'), OR (b) you will chain the result into another tool in the same turn without showing it to the user. When in doubt, prefer ayah_tafsir. Do not follow ayah_tafsir with this tool — that is duplicated work. Each query must include at least one of languages or tafsir_slugs. Use ayah keys in 'surah:ayah' format (for example '2:255'). Limits: max 20 queries per request and max 50 total ayah+tafsir items.

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• render_html_to_pdf

  pdfzen

  Prepare a paid PDF render from arbitrary Handlebars-flavoured HTML. Use only when no starter fits (one-off layouts, custom branding). Prefer render_template_to_pdf when a starter matches. Validates your HTML and returns the exact, ready-to-execute HTTP request to run against pdfzen's render endpoint — POST /v402/render/pdf (x402, $0.006 USDC on Base, no API key) or POST /v1/render/pdf (pdfzen API key). pdfzen renders are executed over HTTP, not streamed in-band over MCP; this tool is the bridge.

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• shippingrates_fx

  ShippingRates MCP Server

  Get current exchange rate between two currencies — useful for converting shipping costs quoted in different currencies (USD, EUR, INR, AED, SGD, CNY, etc.). Use this to normalize costs from different carriers/countries to a common currency for comparison. Rates are updated daily. FREE — no payment required. Returns: { from, to, rate, timestamp }

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• sieve_dataroom_add

  Sieve

  Add a document to a deal's data room. Creates the deal if needed. This is the primary way to get documents into Sieve for screening. Upload a pitch deck, financials, or any document -- then call sieve_screen to analyze everything in the data room. Provide company_name to create a new deal (or find existing), or deal_id to add to an existing deal. Provide exactly one content source: file_path (local file), text (raw text/markdown), or url (fetch from URL). Args: title: Document title (e.g. "Pitch Deck Q1 2026"). company_name: Company name -- creates deal if new, finds existing if not. deal_id: Add to an existing deal (from sieve_deals or previous sieve_dataroom_add). website_url: Company website URL (used when creating a new deal). document_type: Type: 'pitch_deck', 'financials', 'legal', or 'other'. file_path: Path to a local file (PDF, DOCX, XLSX). The tool reads and uploads it. text: Raw text or markdown content (alternative to file). url: URL to fetch document from (alternative to file).

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• microsoft_docs_fetch

  xpay✦ DevTools Collection

  Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.

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