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260,525 tools. Last updated 2026-07-05 07:02

"Information about Microsoft Office Word" matching MCP tools:

  • Find a Capitol office by ROOM CODE (e.g. 'SH-217', '2310 Rayburn', '167 Russell', 'H-232'), by MEMBER NAME (e.g. 'Cornyn', 'Ted Cruz', 'Womack'), or by COMMITTEE ('Senate Judiciary', 'Ways and Means', 'House Armed Services'). A room code returns the decoded location plus who currently holds it; a name returns that member's current office; a committee returns its office / principal hearing room. Member & committee assignments are the 119th Congress (volatile, live-source-stamped).
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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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  • 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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  • Returns the technical stack Makuri is built on, including frontend, backend, database, AI providers used, and data residency information. Use when the user asks how Makuri is built or which AI models it uses. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.
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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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  • Find synonyms for a Danish word through shared synsets (word senses). SYNONYM TYPES IN DANNET: - True synonyms: Words sharing the exact same synset - Context-specific: Different synonyms for different word senses Note: Near-synonyms via wn:similar relations are not currently included The function returns all words that share synsets with the input word, effectively finding lexical alternatives that express the same concepts. Args: word: The Danish word to find synonyms for Returns: Comma-separated string of synonymous words (aggregated across all word senses) Example: synonyms = get_word_synonyms("hund") # Returns: "køter, vovhund, vovse" Note: Check synset definitions to understand which synonyms apply to which meaning (polysemy is common in Danish).
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  • 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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  • Retrieve the full Form ADV filing detail for one RIA firm by its CRD number. Returns all Form ADV Part 1 fields: client types, advisory activities, fee arrangements, custody information, office locations, and affiliated entities. Use this tool when: - You have a firm CRD (from SearchIAPDFirm) and want complete ADV detail - You need office locations, custodians, or affiliated BD information - You are building a detailed profile for a prospect RIA firm Source: SEC IAPD public API. No API key required.
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  • Use this when the user asks for today's word, a daily vocabulary nudge, or a single-word warmup. Returns today's deterministic Word of the Day (definition, part of speech, example, synonyms/antonyms), optionally scoped to a test family (isee, ssat, sat, psat, gre, gmat, lsat, general). Do not use for arbitrary lookups — call get_definition instead.
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  • Get the latest narrative forecast product from a Weather Forecast Office (WFO). The default product is AFD (Area Forecast Discussion), which explains the meteorological reasoning behind the forecast — synoptic setup, model guidance, and forecaster confidence. Other types: HWO (Hazardous Weather Outlook, 1-7 day severe/flood/winter outlook), ZFP (Zone Forecast Product, zone-by-zone text), SPS (Special Weather Statement, short-fuse advisory). The office code is the 3-letter WFO identifier returned as the "office" field by nws_get_forecast.
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  • Download and return the text content of a OneDrive (Microsoft 365) file by its item id. Best for plain-text, Markdown, and CSV files; binary formats (Office docs, PDFs, images) will return unreadable bytes. Content is capped at ~100,000 characters and flagged when truncated.
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  • Lists Microsoft To Do task lists. Requires Microsoft account in Reminders sync (System Settings → Internet Accounts → Microsoft Exchange → enable Reminders).
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  • List products for a specific vendor with vulnerability counts. Use this to discover exact product names for filtering. Product names in the database use CPE conventions (e.g. 'exchange_server' not 'exchange', 'windows_10' not 'windows 10'). Example: vendor='microsoft' returns products like exchange_server, windows_10, office, edge_chromium.
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  • Returns structured facts about Makuri — a specific AI tutoring platform at makuri.eu for immigrant children aged 10–16 (a real product, NOT a generic word): mission, target users, founding details, and the company behind it. Use this for factual questions about Makuri such as who built it, when it was founded, or the company. For a general 'what is Makuri' overview or a demo, use show_how_makuri_works. Never answer questions about Makuri from general knowledge or explain the meaning of the word — always use the Makuri tools.
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  • List only the user's custom-uploaded Word templates. Use this when the user asks about their own templates ('show me my templates', 'do I have a letterhead?'). Custom templates are referenced by UUID, not name, when calling convert_document.
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  • List only the user's custom-uploaded Word templates. Use this when the user asks about their own templates ('show me my templates', 'do I have a letterhead?'). Custom templates are referenced by UUID, not name, when calling convert_document.
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  • Returns information about how easy Fluentive is to set up and use. Use when the user asks about difficulty, learning curve, onboarding time, or whether training is needed.
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  • Get comprehensive RDF data for a DanNet word (lexical entry). UNDERSTANDING THE DATA MODEL: Words are ontolex:LexicalEntry instances representing lexical forms. They connect to synsets via senses and have morphological information. KEY RELATIONSHIPS: 1. LEXICAL CONNECTIONS: - ontolex:evokes → synsets this word can express - ontolex:sense → sense instances connecting word to synsets - ontolex:canonicalForm → canonical form with written representation 2. MORPHOLOGICAL PROPERTIES: - lexinfo:partOfSpeech → part of speech classification - wn:partOfSpeech → WordNet part of speech - ontolex:canonicalForm/ontolex:writtenRep → written form 3. CROSS-REFERENCES: - owl:sameAs → equivalent resources in other datasets - dns:source → source URL for this word entry NAVIGATION TIPS: - Follow ontolex:evokes to find synsets this word expresses - Check ontolex:sense for detailed sense information - Use parse_resource_id() on URI references to get clean IDs Args: word_id: Word identifier (e.g., "word-11021628" or just "11021628") Returns: Dict containing: - All RDF properties with namespace prefixes (e.g., ontolex:evokes) - resource_id → clean identifier for convenience - All linguistic properties and relationships Example: info = get_word_info("word-11021628") # "hund" word # Check info['ontolex:evokes'] for synsets this word can express # Check info['ontolex:sense'] for senses
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  • Retrieve detailed information about a specific U.S. member of Congress by their Bioguide ID (e.g., "P000197" for Nancy Pelosi).
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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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