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306,671 tools. Last updated 2026-07-27 03:59

"A server for finding information and resources about Word DOCX files" matching MCP tools:

  • Use this tool whenever the user shares a Word document (.docx) and wants to read, review, summarise, or analyse its content. Triggers: 'read this Word file', 'what does this doc say', 'summarise this document', 'extract text from this .docx'. Accepts base64-encoded .docx. Returns full text, paragraph count, word count, and character count. Works with Word, Google Docs exports, and LibreOffice files.
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  • Use this tool whenever the user shares a Word document (.docx) and wants to read, review, summarise, or analyse its content. Triggers: 'read this Word file', 'what does this doc say', 'summarise this document', 'extract text from this .docx'. Accepts base64-encoded .docx. Returns full text, paragraph count, word count, and character count. Works with Word, Google Docs exports, and LibreOffice files.
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  • Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.
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  • Create a Revise document from a file at a public http(s) URL (.md, .markdown, .txt, .html, .htm, .docx, .rtf, .odt; PDFs/images not yet supported). The server fetches the URL — file bytes are never passed inline. For a LOCAL file, use upload_document instead (it streams the file to the server). Returns the new document id and URL. Returns url (give it to your user — they view the document and create a free account to keep it, in one step) and edit_token (your Bearer token for future edits). The document is private and deleted after 7 days if unclaimed.
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  • Offload a document conversion to Botverse — runs server-side in seconds, returns a download link, and frees you to continue with other tasks while it processes. Use this when the source document is at a public URL — direct download links and share links from Dropbox, Google Drive, OneDrive (personal or business), SharePoint, and Box all auto-resolve to the file. If you already have the content as a string, use convert_content instead — no upload step needed. Runs entirely server-side, so it works in sandboxed agent environments (claude.ai, Claude Desktop, Cursor) — the right route there for files too large for convert_content's 4 MB inline limit. Supported inputs: md, html, rst, txt, docx. Supported outputs: docx (Word), pdf, html, txt, md, rst, xlsx (tables extracted). Returns a job_id immediately. Poll get_job_status every 5s until 'complete', then get_output_content (inline, sandbox-safe) or get_download_url (S3 link). Flat fee $0.05 per file.
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  • Hands a 12- or 24-word seed phrase to NFPT's orchard-scanner CLI, returns the matching UFVK. FREE but rate-limited to 6/minute/IP. Be loud about the security trade-off: the phrase transits our server (no logging, no persistence) but a network observer between you and us would see the bytes. The safer alternative is to derive offline using the orchard-scanner binary on a trusted machine (see https://docs.seneschal.space/derive-locally). A UFVK is read-only; it cannot spend funds.
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    Enables creating editable MathType 7 equations in Microsoft Word and PowerPoint with native numbering and cross-references.
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    Enables AI agents to manage citations in Microsoft Word via EndNote, including DOI-verified paper search, RIS import/export, and genuine CWYW IEEE citations.
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Matching MCP Connectors

  • Look a Latin word up on Wiktionary and return its senses plus full declension/conjugation tables — attested content, not invented. Any form of the word works; an inflected query is resolved to its lemma automatically (via previously cached paradigms or the Morpheus analyzer) and the result notes the resolution. With search_language='eng' the query is an English word instead: the result lists its per-sense Latin equivalents (the translations block) plus their expanded entries. Returns Markdown plus the same result as structuredContent matching the declared outputSchema. Results are cached server-side; first-time queries reach the live upstream politely and calls are rate limited — on a rate-limit error, wait a few seconds and retry. Content is from en.wiktionary.org (CC BY-SA 4.0 — attribute and share alike if republished).
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  • Transcribe an already-uploaded video/audio file (from get_upload_url) into a speaker-labelled transcript. Same one-call pipeline and options as transcribe_from_url (attendee naming, srt/vtt, formatted docx/pdf). Use for local files or files larger than a URL fetch allows (up to 2 GB). CONSENT: you must have all parties' consent. Poll get_job_status (live stage) until complete, then get_download_url / get_output_content. ~$3 per hour of audio. RELIABILITY & FALLBACK: Botverse returns structured errors with a message. A bare 'HTTP 500' or 'tool call failed' with no message body is the MCP connector / sandbox layer between your client and Botverse — NOT a Botverse outage (the service is up if get_wallet_balance succeeds). If MCP calls keep failing, do NOT write your own ffmpeg / transcription / document-conversion code as a workaround — that wastes tokens and reinvents what Botverse does. Instead, tell the user to run the equivalent Botverse CLI from their own (non-sandboxed) machine, e.g. `npx botverse transcribe <file> --to docx` (also `transcode` and `convert`), then retry the MCP call.
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  • Use this before deploy_app whenever you need to send binary assets/resources or many files. Prefer this whenever the source of truth already exists as local files or a local project tree, especially for multiple files or whole-file replacements. This is the required path for images, backgrounds, icons, fonts, PDFs, media, archives, and other file assets you want deployed. Prepare local files and the upload manifest first. Call upload_assets only when you can upload immediately. If the upload_url expires before use or the upload fails because it expired, call upload_assets again to get a fresh upload_url and upload_id. PUT multipart/form-data to the returned upload_url: a 'payload' part (JSON manifest with text file changes/diffs and deletePaths) plus binary files named by app-relative path. The upload manifest carries all text changes and diffs plus deletePaths. After upload succeeds, call deploy_app with upload_id only. Do not also send files[] or deletePaths[]; those changes belong in the upload manifest. Never base64-encode binary assets into deploy_app.
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  • Get synsets (word meanings) for a Danish word, returning a sorted list of lexical concepts. DanNet follows the OntoLex-Lemon model where: - Words (ontolex:LexicalEntry) evoke concepts through senses - Synsets (ontolex:LexicalConcept) represent units of meaning - Multiple words can share the same synset (synonyms) - One word can have multiple synsets (polysemy) This function returns all synsets associated with a word, effectively giving you all the different meanings/senses that word can have. Each synset represents a distinct semantic concept with its own definition and semantic relationships. Common patterns in Danish: - Nouns often have multiple senses (e.g., "kage" = cake/lump) - Verbs distinguish motion vs. state (e.g., "løbe" = run/flow) - Check synset's dns:ontologicalType for semantic classification DDO CONNECTION AND SYNSET LABELS: Synset labels are compositions of DDO-derived sense labels, showing all words that express the same meaning. For example: - "{hund_1§1; køter_§1; vovhund_§1; vovse_§1}" = all words meaning "domestic dog" - "{forlygte_§2; babs_§1; bryst_§2; patte_1§1a}" = all words meaning "female breast" Each individual sense label follows DDO structure: - "hund_1§1" = word "hund", entry 1, definition 1 in DDO (ordnet.dk) - "patte_1§1a" = word "patte", entry 1, definition 1, subdefinition a - The § notation connects directly to DDO's definition numbering system This composition reveals the semantic relationships between Danish words and their shared meanings, all traceable back to authoritative DDO lexicographic data. RETURN BEHAVIOR: This function has two possible return modes depending on search results: 1. MULTIPLE RESULTS: Returns List[SearchResult] with basic information for each synset 2. SINGLE RESULT (redirect): Returns full synset data Dict when DanNet automatically redirects to a single synset. This provides immediate access to all semantic relationships, ontological types, sentiment data, and other rich information without requiring a separate get_synset_info() call. The single-result case is equivalent to calling get_synset_info() on the synset, providing the same comprehensive RDF data structure with all semantic relations. Args: query: The Danish word or phrase to search for language: Language for labels and definitions in results (default: "da" for Danish, "en" for English when available) Note: Only Danish words can be searched regardless of this parameter Returns: MULTIPLE RESULTS: List of SearchResult objects with: - word: The lexical form - synset_id: Unique synset identifier (format: synset-NNNNN) - label: Human-readable synset label (e.g., "{kage_1§1}") - definition: Brief semantic definition (may be truncated with "...") SINGLE RESULT: Dict with complete synset data including: - All RDF properties with namespace prefixes (e.g., wn:hypernym) - dns:ontologicalType → semantic types with @set array - dns:sentiment → parsed sentiment (if present) - synset_id → clean identifier for convenience - All semantic relationships and linguistic properties Examples: # Multiple results case results = get_word_synsets("hund") # Returns list of search result dictionaries for all meanings of "hund" # => [{"word": "hund", "synset_id": "synset-3047", ...}, ...] # Single result case (redirect) result = get_word_synsets("svinkeærinde") # Returns complete synset data for unique word # => {'wn:hypernym': 'dn:synset-11677', 'dns:sentiment': {...}, ...}
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  • Return a 15-minute presigned download URL for a report in the requested binary format. `format=md` presigns the cached markdown — instant, no compute. `format=docx` returns a branded Word document with a cover page (title as the hero, the named analyst credited directly beneath it, a small 'Built on Valuein' credit linked to valuein.biz) followed by the report body on page 2 (masthead repeating the analyst's name, abstract, sections, citations table with clickable SEC EDGAR links), with a running footer (ticker, page number, a single disclosure line) repeated on every page. The DOCX is cached in R2 alongside the markdown after first build so repeat downloads are instant; pass `force_regenerate: true` to bust the cache (e.g. right after `update_report`). Tier gate mirrors `get_report`: authors always see their own reports; non-authors below the report's required tier get an upgrade prompt.
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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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  • Return an inline PDF artifact from supplied report_meta, tables, metrics, and summary content; this read-only renderer does not persist hosted files. Use this only when a structured report payload already exists; use report_docx_generate for editable Word output or compliance_edd_report to build the memo first.
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  • Obtain the CivilQuants customer-side document pipeline — the toolkit the document-heavy skills (tender review, geotechnical / geo-environmental interpretation) use to chunk a tender pack and render a Word pack on the user's machine. Returns the self-unpacking chunking package, the pipeline discipline, and the python-docx render helpers. Universal (free + paid). NOTE: running the pipeline over real documents requires a code-execution client (Claude Code / Codex / VS Code) — a chat connector can read the toolkit but cannot execute it. The full kit is large (~60 KB); pass component='chunking'|'discipline'|'render' for one part (~20 KB each), or omit it for the whole kit.
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  • Full-text search across all Gonka documentation files. Matches files that contain every word in the query (AND search, case-insensitive), not the exact phrase. Use this when query_graph() returns no results.
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  • List all available Quantustik MCP tools and resources. Use this as the entry point when a user asks what can you do with Quantustik, or to discover the full surface area of the server. Returns a structured description of every tool and resource.
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  • Get comprehensive RDF data for a DanNet sense (lexical sense). UNDERSTANDING THE DATA MODEL: Senses are ontolex:LexicalSense instances connecting words to synsets. They represent specific meanings of words with examples and definitions. KEY RELATIONSHIPS: 1. LEXICAL CONNECTIONS: - ontolex:isSenseOf → word this sense belongs to - ontolex:isLexicalizedSenseOf → synset this sense represents 2. SEMANTIC INFORMATION: - lexinfo:senseExample → usage examples in context - rdfs:label → sense label (e.g., "hund_1§1") 3. REGISTER AND STYLISTIC INFORMATION: - lexinfo:register → formal register classification (e.g., ":lexinfo/slangRegister") - lexinfo:usageNote → human-readable usage notes (e.g., "slang", "formal") 4. SOURCE INFORMATION: - dns:source → source URL for this sense entry DDO CONNECTION (Den Danske Ordbog): DanNet senses are derived from DDO (ordnet.dk), the authoritative modern Danish dictionary. SENSE LABELS: The format "word_entry§definition" connects to DDO structure: - "hund_1§1" = word "hund", entry 1, definition 1 in DDO - "forlygte_§2" = word "forlygte", definition 2 in DDO - The § notation directly corresponds to DDO's definition numbering SOURCE TRACEABILITY: The dns:source URLs link back to specific DDO entries: - Format: https://ordnet.dk/ddo/ordbog?entry_id=X&def_id=Y&query=word - Note: Some DDO URLs may not resolve correctly if IDs have changed since import - If the DDO page loads correctly, the relevant definition has CSS class "selected" METADATA ORIGINS: Usage examples, register information, and definitions flow from DDO's corpus-based lexicographic data, providing authoritative linguistic information. NAVIGATION TIPS: - Follow ontolex:isSenseOf to find the parent word - Follow ontolex:isLexicalizedSenseOf to find the synset - Check lexinfo:senseExample for usage examples from DDO corpus - Check lexinfo:register and lexinfo:usageNote for stylistic information - Use dns:source to attempt tracing back to original DDO definition (with caveats) - Use parse_resource_id() on URI references to get clean IDs Args: sense_id: Sense identifier (e.g., "sense-21033604" or just "21033604") Returns: Dict containing: - All RDF properties with namespace prefixes (e.g., ontolex:isSenseOf) - resource_id → clean identifier for convenience - All sense properties and relationships Example: info = get_sense_info("sense-21033604") # "hund_1§1" sense # Check info['ontolex:isSenseOf'] for parent word # Check info['ontolex:isLexicalizedSenseOf'] for synset # Check info['lexinfo:senseExample'] for usage examples from DDO # Check info['lexinfo:register'] for register classification # Check info['lexinfo:usageNote'] for usage notes like "slang" # Check info['dns:source'] for DDO source URL (may not always work)
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  • Get autocomplete suggestions for Danish word prefixes. Useful for discovering Danish vocabulary or finding the correct spelling of words. Returns lemma forms (dictionary forms) of words. Args: prefix: The beginning of a Danish word (minimum 3 characters required) max_results: Maximum number of suggestions to return (default: 10) Returns: Comma-separated string of word completions in alphabetical order Note: Autocomplete requires at least 3 characters to prevent excessive results. Example: suggestions = autocomplete_danish_word("hyg", 5) # Returns: "hygge, hyggelig, hygiejne"
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  • Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.
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  • Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.
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