"A tool for processing DOC documents" matching MCP tools:

• 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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• update_doc

  Dock

  Replace a workspace's doc body. Takes EITHER TipTap JSON (`content`) OR Markdown (`markdown`): pass markdown when you're producing prose from scratch (CommonMark + GFM is the format every LLM emits natively), pass TipTap JSON when you need structural edits to an existing doc (round-trip from get_doc, mutate, write back). Beyond CommonMark + GFM, the markdown layer recognizes: - **** → inline image. Use ANY publicly-reachable URL (HTTPS preferred — HTTP fires browser mixed-content warnings; data: URIs are rejected by `allowBase64: false`). Renders block-feeling via CSS (max-width 100%, rounded corners, drop shadow) even though the underlying node is inline. The `alt` text is the accessible label and shows in place of the image if the URL fails to load — always include it. To attach a user-uploaded file, hit `POST /api/workspaces/:slug/upload-image` from the human-side UI first to get a Vercel Blob URL, then reference that URL in the doc markdown. - A **lone video-file URL on its own line** (extension `.mp4` / `.m4v` / `.webm` / `.mov` / `.mkv`, signed-params + timestamp fragments tolerated) → native HTML5 `<video controls preload="metadata">` player. Source URL is referenced directly: no iframe, no transcoding, no quality loss. Vercel Blob is the canonical hosting (5 GB per file, served with HTTP range requests so 4K masters stream cleanly), but ANY publicly-reachable HTTPS URL works. Sample shape: a paragraph containing only `https://cdn.dock.ai/2025-launch-walkthrough.mp4`. Mid-paragraph URLs stay as plain links — surrounding prose disqualifies the auto-promotion (matches the oEmbed convention). - **```mermaid** fenced code → diagram (15 sub-types: flowchart, sequence, gantt, ER, state, class, mindmap, timeline, pie, quadrant, sankey, XY-chart, packet, block, journey) - **$x$** inline math, **$$x$$** block math (LaTeX, KaTeX-rendered, scripts/href disabled) - **> [!NOTE]** / **[!TIP]** / **[!IMPORTANT]** / **[!WARNING]** / **[!CAUTION]** GFM-style callouts - **```svg** fenced code → sanitized SVG embed (the universal escape hatch for custom diagrams; scripts and event handlers stripped at write time) - **<details><summary>X</summary>BODY</details>** → collapsible toggle - **[[slug]]** / **[[org/slug]]** / **[[slug#tab]]** / **[[slug#row-id]]** / **[[slug|display]]** → cross-references to another workspace, surface, or row. Resolved against your accessible workspace set; targets you can't see render as plain text on the reader's side (no info leak). Every cross-ref creates a Backlink row so the target's 'referenced from' sidebar shows this doc. - **[@Label](dock:mention/<kind>/<id>)** → @-mention of a user or agent. `<kind>` is `agent` or `human`; `<id>` is the principal id. Optional query params `?org=<slug>` (agents) or `?email=<addr>` (humans) for renderer hints. Mentioning a human writes a `doc_mention` row to their inbox + sends a deep-link email; mentioning an agent fires the `doc.mention_added` webhook so the agent service can wake up and reply. Re-saving a doc that already mentions someone does NOT re-fire — only newly-added mentions notify (computed from a diff against the previous body). Use this from agent code to ping a teammate when a doc you wrote needs their eyes. - A **lone URL on its own line** from a safelisted provider (YouTube, Vimeo, Loom, Figma, CodePen, GitHub gists) → sandboxed iframe embed. Other URLs stay as regular links. Surrounding prose disqualifies the auto-embed. Per-format caps: max 50 Mermaid diagrams (30 KB source each), max 500 math expressions (8 KB source each), max 50 SVG blocks (100 KB source each post-sanitize), max 200 cross-refs per doc, max 500 @-mentions per doc, max 20 embeds per doc, max 20 videos per doc (5 GB per file at upload time), max 200 images per doc. See /docs/doc-formats for examples. Last-write-wins; no CRDT merge. Emits doc.updated + doc.heading_added + doc.mention_added events as applicable. Requires editor role. Multi-surface workspaces optionally accept `surface_slug` to write to a specific doc tab; omitted writes the primary doc surface. Append-only updates have a dedicated `append_doc_section` tool that doesn't require fetching the body first.

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• word_create

  local-mcp

  Creates a new Word (.docx) document at `path` with the given text content (and an optional title rendered as the heading). Requires confirm=true — called without it, returns a preview of what will be written instead of creating the file. The path must be somewhere Local MCP 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.

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• tela_get_doc_content

  DERO MCP Server

  Composite: fetch the actual file content stored in a TELA-DOC-1 contract. A DOC's file (HTML/CSS/JS/...) lives inside a DVM-BASIC comment block in the contract code — NOT in a stored variable — so this tool fetches DERO.GetSC, confirms the SCID is a DOC, and extracts the file bytes. Gzip-compressed files (a `.gz` filename, the TELA-CLI default) are transparently base64-decoded + decompressed to plaintext. Large files paginate via offset. When to call: when a user wants to READ or inspect the actual code/markup a TELA app file holds (e.g. "show me the HTML of this TELA DOC", "what does this app's app.js contain"). Get DOC SCIDs from tela_inspect on an INDEX first. PREFER this over dero_get_sc: that returns the raw DVM contract wrapper; this extracts just the embedded file content and reports docType, size, and signature presence. Input Requirements: - `scid` is REQUIRED. Must be 64 hex chars and reference a TELA-DOC-1 contract (an INDEX or non-TELA SCID returns INVALID_INPUT with guidance). - `offset` is OPTIONAL. Byte offset into the extracted content; pass `next_offset` to read the next chunk of a large file. - `topoheight` is OPTIONAL. Omit for the latest committed state. Output: `{ scid, topoheight, filename, doc_type, sub_dir, content_embedded, content, content_offset, content_length, content_truncated, next_offset, compressed, decompressed, stored_filename, signature, signature_note, note, narrative, related_docs }`. `content` is the plaintext file (a 60000-char chunk; paginate via `next_offset`), or null when content is not embedded (DocShard/STATIC/external). `compressed` is true for `.gz` files; `decompressed` is true when this tool gunzipped them (`filename` then strips `.gz`; `stored_filename` keeps the on-chain name). The contract's author signature presence is reported but NOT cryptographically verified.

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• check_pack

  OpenWarrant — Document Verification Suite

  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 classify_document; 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.

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• check_job_status

  Sats4AI - Bitcoin-Powered AI Tools

  Poll the status of an async job. Use this after calling any async tool (generate_video, animate_image, generate_3d_model, transcribe_audio, epub_to_audiobook, ai_call) that returns a requestId. Returns JSON: { status: 'queued' | 'processing' | 'completed' | 'failed', requestId, jobType }. For epub-audiobook, also includes progress (0-100) and chapterProgress array. Poll every 5-10 seconds. When status is 'completed', call get_job_result to retrieve the output. When status is 'failed', the response includes an error message — do not retry automatically. This tool is free and does not require payment. Do NOT use for synchronous tools (generate_image, generate_text, etc.) — those return results immediately.

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• check_website

  lexvibe

  Free legal-compliance check of a public website (no signup). Fetches the URL server-side and detects data processing relevant to compliance — analytics, marketing pixels, payments, generative AI, email collection, third-party sharing — then returns the legal documents and cookie-consent setup the site needs, whether the EU AI Act applies, and suggestedAnswers you can pass straight to generate_policies. Result contract: `fetched` is true only when the page HTML was actually read; when false, `fetchError` says why ("unreachable": the URL could not be resolved or connected; "blocked": the server answered with an error status) and the detected signals are NOT meaningful — report the check as inconclusive, not as clean. Run it again after adding any SDK, analytics, payment, auth or AI integration: when an appId is passed (or read from the installed LexVibe snippet) the result ALWAYS includes a `drift` key — status "in_sync", "outdated" (listing processing the hosted legal documents don't cover yet) or "unavailable" with a bounded `reason` (no-database, app-not-found, no-baseline, domain-mismatch, page-not-fetched) when the comparison could not be made; treat "unavailable" as unknown, never as in sync. Read-only.

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• create_workspace

  Dock

  Create a new workspace in the caller's org. Works for both user and agent callers; agent-created workspaces attribute to the agent and enroll the agent's owning user as a co-owner so the human sees it in their dashboard. The new workspace is seeded with one primary surface matching `mode`: `doc` → a Notes tab (for prose), `table` → a Sheet tab (for records), `html` → a Mockup tab (sandboxed HTML preview). Decide the surface before you create: prose (briefs, notes, summaries, drafts) → `doc`; records with shared columns (tasks, leads, rows) → `table`. If you omit `mode`, pass `initial_markdown` to signal a `doc`; with neither `mode` nor `initial_markdown`, an agent caller gets a guided error asking it to choose `doc` or `table` (so you never silently land on the wrong surface). An explicit `mode` is always honored. `html` is only picked when explicitly requested. Add more tabs of any kind later via `create_surface`. Agent-created workspaces default to org-visibility so sibling agents in the same org aren't 403'd. For prose content (briefs, summaries, changelogs) pass `initial_markdown` to seed the doc body in one call; the markdown is converted server-side, no need to hand-build ProseMirror JSON.

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• get_analysis_status

  hypathesis

  Check the processing status of an uploaded paper. Poll this tool after uploading a PDF until status is 'Ready' before calling get_variable_relationships. Args: file_id: The file_id returned by the /upload endpoint. authorization: Optional. API key as 'Bearer hk_...' or 'hk_...'. Returns: { "status": "Processing" | "Ready" | "Empty" | "Ineligible" | "Pending", "edges_count": int, "variables_count": int }

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• get_turnaround_estimate

  Andrii Co. Notary & Apostille Assistant

  Estimate typical completion + shipping timing for a service (apostille processing days, mail-in and delivery legs). Ranges, not guarantees; no side effects. For an exact appointment time use search_availability.

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• browse

  RedM Mcp

  Enumerate doc paths in a category/namespace. Use to discover what exists before calling `get_document` or a targeted `grep_docs`. NOT a content search — use `semantic_search` for behavior/concept lookups or `grep_docs` for token lookups. Returns `{path, title, chunks}[]`.

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• detect_stack_from_text

  StackSwap

  Infer a GTM stack from a freeform text blob (a careers page, job posting, public site HTML, RFP, 'What we use' doc, browser DevTools network tab, etc.). Returns ranked tool matches with confidence levels (high/medium/low) and evidence snippets, plus a ready-to-use array for chaining into `scan_stack` or `find_overlaps`. Use when the user says 'I don't know what we use' or pastes a competitor's careers page to scout. Conservative on ambiguous short tokens — multi-mention or canonical-name matches win.

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• get_content_details

  flevy

  Full metadata for one Flevy item, by content_id from search_content (e.g. "doc-1234" or "case-567"). Documents return the author with their credentials (headline, bio, LinkedIn, profile URL; pass the author name to search_content's author filter to list more of their documents), full description, editor summary, AI summary, and editorial review when available, page/slide count, price, FlevyPro inclusion, management topics, ranking badge, and the number of slide deep dives available. Case studies return the client situation, TL;DR, and summary. Call this before recommending an item so you can describe it accurately and cite the author's credentials, and share the returned flevy.com URL.

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• get_slide_deep_dive

  flevy

  Slide-by-slide preview of a Flevy document (a "doc-<n>" content_id). Returns every showcased slide with its name, a text description of what the slide contains (you cannot see the image, so use the description), a preview image URL, and a deep link to that slide on flevy.com. Use when a user wants to know what is inside a specific presentation before purchasing, or to reference an individual slide. Only some documents have deep dives; get_content_details reports the count.

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• aetherwave_remove_background_video

  AetherWave Studio

  Strips the background from a video frame-by-frame using rembg (u2netp) on AetherWave's Python service. Pass a public `videoUrl`. Choose `bgType: "transparent"` for an alpha-channel WebM output (compositing) or `bgType: "color"` with a `customColor` hex for a solid replacement. 2 credits per second. Slowest tool in the surface (per-frame processing); a 6s clip takes ~4 min, a 30s clip ~15-20 min. Works best on subjects with clear edges (people, products). Returns the processed video URL (R2-hosted).

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• generate_policies

  lexvibe

  Generate the legal documents (privacy policy, terms of service and, if applicable, an AI disclosure) localized and tailored to the target markets (GDPR, UK GDPR, CCPA…). Returns Markdown drafts. Pass check_website's or check_store's suggestedAnswers as `answers` so the documents disclose the right processing. Anonymous remote generation is template-based and capped at 3 locales; AI-tailored, hosted and auto-updated documents require a LexVibe account (https://golexvibe.com).

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• search_docs

  Devdocs Io

  Find which documentation SETS exist whose NAME matches a substring (e.g. "python" → Python 3.x, "react" → React). Returns doc SETS, NOT their content — this does NOT look up a function/method/API name. To search inside a doc for an entry like "Array.map" or "fetch", use search_index (slug + query).

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• resolve_topic

  Pine Script

  USE WHEN looking up an exact Pine Script API term or known concept keyword. Returns the best-matching doc paths with matched keywords and a retrieval suggestion (get_doc or list_sections + get_section). AFTER calling this tool, follow the suggestion: call get_doc() for small files or list_sections() + get_section() for large files. For natural language questions use search_docs() instead. Data sourced from bundled TOPIC_MAP and doc file content scan.

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• validate_rfc_mx

  MCP LatAm Tools

  Validates a Mexican RFC (Registro Federal de Contribuyentes) format for both individuals (13 characters) and companies (12 characters). Use this tool when processing Mexican invoices (CFDI), tax forms, supplier registrations, or any document requiring a valid Mexican taxpayer identifier. Returns whether the RFC format is valid, the detected type (individual or company), and the cleaned RFC. Note: validates format only, does not verify against the SAT registry.

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• verify_identity

  OpenWarrant — Document Verification Suite

  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.

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