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136,038 tools. Last updated 2026-05-18 00:37

"Definition or information about the word 'word'" matching MCP tools:

  • Transcribe audio or video to text, including per-word timestamps for precise editing. Three-call flow: (1) call with `filename` to receive {job_id, payment_challenge}; (2) pay via MPP, then call with `job_id` + `payment_credential` to receive {upload_url} (presigned PUT, 1h expiry); (3) PUT the bytes, then complete_upload(job_id), then poll get_job_status(job_id). On completion, get_job_status returns presigned download URLs for two files: role `transcript` (SRT) and role `transcript-words` (JSON matching /.well-known/weftly-transcript-v2.schema.json, with segment-level and per-word timestamps). For other formats, pass `format=srt|txt|vtt|json|words` to get_job_status to receive content inline — `txt` and `vtt` are derived from SRT, `json` is v1 (segments only), `words` is v2 (segments + words). Flat price: audio $0.50, video $1.00 — see /.well-known/mpp.json for the authoritative table. Use for podcasts, interviews, meetings, lectures, and especially for creating clips, multicamera edits, or edit-video-from-transcript where word boundaries matter. Retrying any call with `job_id` alone returns current state (idempotent). Failed jobs auto-refund.
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  • Get a complete overview of all senses for a Danish word in a single call. Replaces the common pattern of calling get_word_synsets → get_synset_info per result → get_word_synonyms, collapsing 5-15 HTTP round-trips into one SPARQL query. Only returns synsets where the word is a primary lexical member (i.e. the word itself has a direct sense in the synset), excluding multi-word expressions that merely contain the word as a component. Args: word: The Danish word to look up Returns: List of dicts, one per synset, each containing: - synset_id: Clean synset identifier (e.g. "synset-3047") - label: Human-readable synset label - definition: Synset definition (may be truncated with "…") - ontological_types: List of dnc: type URIs - synonyms: List of co-member lemmas (true synonyms only) - hypernym: Dict with synset_id and label of the immediate broader concept, or null - lexfile: WordNet lexicographer file name (e.g. "noun.animal"), or null if absent Example: overview = get_word_overview("hund") # Returns list of 4 synsets, the first being: # {"synset_id": "synset-3047", # "label": "{hund_1§1; køter_§1; vovhund_§1; vovse_§1}", # "definition": "pattedyr som har god lugtesans ...", # "ontological_types": ["dnc:Animal", "dnc:Object"], # "synonyms": ["køter", "vovhund", "vovse"], # "lexfile": "noun.animal"} # Pass synset_id to get_synset_info() for full JSON-LD data on any result: # full_data = get_synset_info(overview[0]["synset_id"])
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  • Returns information about safety features on Makuri, including age verification, content filtering, parental controls, and AI safety guardrails. Use when the user asks about child safety, content moderation, or how Makuri protects minors.
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  • Return a ~500-word educational explainer of M/M/c queueing theory: Little's Law, utilization, why averages mislead, how simulation relates to Erlang-C. No inputs. Use this when the user asks a conceptual 'why' or 'how does this work' question rather than asking for a number.
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  • Get comprehensive RDF data for any entity in the DanNet database. Supports both DanNet entities and external vocabulary entities loaded into the triplestore from various schemas and datasets. UNDERSTANDING THE DATA MODEL: The DanNet database contains entities from multiple sources: - DanNet entities (namespace="dn"): synsets, words, senses, and other resources - External entities (other namespaces): OntoLex vocabulary, Inter-Lingual Index, etc. All entities follow RDF patterns with namespace prefixes for properties and relationships. NAVIGATION TIPS: - DanNet synsets have rich semantic relationships (wn:hypernym, wn:hyponym, etc.) - External entities provide vocabulary definitions and cross-references - Use parse_resource_id() on URI references to get clean IDs - Check @type to understand what kind of entity you're working with Args: identifier: Entity identifier (e.g., "synset-3047", "word-11021628", "LexicalConcept", "i76470") namespace: Namespace for the entity (default: "dn" for DanNet entities) - "dn": DanNet entities via /dannet/data/ endpoint - Other values: External entities via /dannet/external/{namespace}/ endpoint - Common external namespaces: "ontolex", "ili", "wn", "lexinfo", etc. Returns: Dict containing JSON-LD format with: - @context → namespace mappings (if applicable) - @id → entity identifier - @type → entity type - All RDF properties with namespace prefixes (e.g., wn:hypernym, ontolex:evokes) - For DanNet synsets: dns:ontologicalType and dns:sentiment (if applicable) - Entity-specific convenience fields (synset_id, resource_id, etc.) Examples: # DanNet entities get_entity_info("synset-3047") # DanNet synset get_entity_info("word-11021628") # DanNet word get_entity_info("sense-21033604") # DanNet sense # External vocabulary entities get_entity_info("LexicalConcept", namespace="ontolex") # OntoLex class definition get_entity_info("i76470", namespace="ili") # Inter-Lingual Index entry get_entity_info("noun", namespace="lexinfo") # Lexinfo part-of-speech
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  • Create a new Kochava FAA (Free App Analytics) account. IMPORTANT: The user MUST explicitly agree to the FAA Terms of Service before account creation. If tos_agreed is False, this tool will return the TOS link and stop — do NOT submit the form. Call kochava_free_app_analytics_get_tos() to retrieve and present the TOS to the user first, then call this tool again with tos_agreed=True once the user confirms agreement. DISPLAY INSTRUCTIONS: When this tool returns a successful response, you MUST display the 'next_steps' field content to the user EXACTLY as written — word-for-word, preserving ALL text, formatting, line breaks, numbering, and bullet points. Do NOT summarize, rephrase, reword, or omit any part of the 'next_steps' content. Every sentence must be shown to the user as-is. FAA Terms of Service: https://s34035.pcdn.co/wp-content/uploads/2023/08/FAA-Web-Sign-Up-TOS-8-15-23.pdf Example (after user reviews and agrees to TOS): kochava_free_app_analytics_create_acc_and_get_auth_key( first_name="Jane", last_name="Smith", email_address="jane@example.com", phone_number="5551234567", company="Acme Corp", website="www.acme.com", company_address_line_1="123 Main St", company_city="Sandpoint", company_region="Idaho", company_postal_code="83864", country="United States", tos_agreed=True )
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  • Connect to the user's catalogue using a pairing code. IMPORTANT: Most users connect via OAuth (sign-in popup) — if get_profile already works, the user is connected and you do NOT need this tool. Only use this tool when: (1) get_profile returns an authentication error, AND (2) the user shares a code matching the pattern WORD-1234 (e.g., TULIP-3657). Never proactively ask for a pairing code — try get_profile first. If the user does share a code, call this tool immediately without asking for confirmation. Never say "pairing code" to the user — just say "your code" or refer to it naturally.
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  • Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.
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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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  • Get detailed CV version including structured content, sections, word count, and audience profile. cv_version_id from ceevee_upload_cv or ceevee_list_versions. Use to inspect CV content before running analysis tools. Free.
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  • Estimate credit cost for a conversion BEFORE running it. Returns word count, page calculation (300 words/page), and a credit breakdown by format and template type. Use this when the user asks 'how much will this cost?' or when you suspect a conversion might exceed their balance — convert_document refuses to run if credits are insufficient, so estimating first is friendlier.
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  • Estimate credit cost for a conversion BEFORE running it. Returns word count, page calculation (300 words/page), and a credit breakdown by format and template type. Use this when the user asks 'how much will this cost?' or when you suspect a conversion might exceed their balance — convert_document refuses to run if credits are insufficient, so estimating first is friendlier.
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  • Keyword search across the Pāli Tipiṭaka (trigram word-similarity). Searches the configured enabled language(s) on the server. Filterable by pitaka and translation edition. 💡 **Hints for the AI client:** The system's canonical reference is Romanised Pāli (from SuttaCentral). If the user asks in a disabled or unsupported language, translate the keyword to **Romanised Pāli (preferred) or English** before calling this tool — e.g. "suffering" → "dukkha", "mindfulness of breathing" → "ānāpānassati". See the server instructions for the enabled language set. 🔍 **Pick the right search tool for the question shape:** - **Term lookup (exact word appearances)** — e.g. "occurrences of `ānāpānassati`": this tool is best (trigram nails the exact word). - **Concept search ("discourses about X")** — e.g. "discourses about mindfulness of breathing": **use `search_hybrid` instead.** Canonical Pāli has two quirks that hurt keyword search for concepts: • Section headings (`Ānāpānapabba`) often use a different word than the teaching body, which uses verb forms (`assasati`, `passasati`, `dīghaṁ`, `rassaṁ`). E.g. DN22's Ānāpānapabba has 16 segments but the word `ānāpāna` appears in only 2 (header + footer) — the actual teaching segments won't match. • Stock phrases (e.g. `So satova assasati, satova passasati`) recur in 10+ suttas, so a keyword query ranks broadly and won't pinpoint the canonical reference. - **General keyword survey** — set `limit≥30` and filter client-side, or call multiple related forms (root verb + noun + compound).
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  • 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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  • Summarize an audio or video file — returns both a text summary AND the full transcript (with per-word timestamps). Do not also call transcribe on the same file. Three-call flow: (1) call with `filename` to receive {job_id, payment_challenge}; (2) pay via MPP, then call with `job_id` + `payment_credential` to receive {upload_url} (presigned PUT, 1h expiry); (3) PUT the bytes, then complete_upload(job_id), then poll get_job_status(job_id). On completion, get_job_status returns presigned download URLs for three files: role `summary` (plain text), role `transcript` (SRT), and role `transcript-words` (JSON matching /.well-known/weftly-transcript-v2.schema.json, with segment-level and per-word timestamps). For other formats, pass `format=srt|txt|vtt|json|words` to get_job_status to receive transcript content inline — `txt` and `vtt` are derived from SRT, `json` is v1 (segments only), `words` is v2 (segments + words). Flat price: audio $0.75, video $1.25 — see /.well-known/mpp.json for the authoritative table. Use for meetings, long-form interviews, lectures, and podcast episodes; the `words` output additionally supports creating clips, multicamera edits, or edit-video-from-transcript. Retrying any call with `job_id` alone returns current state (idempotent). Failed jobs auto-refund.
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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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  • Full-text search across recall reasons and product descriptions using PostgreSQL text search. Finds recalls mentioning specific terms (e.g. 'salmonella contamination', 'mislabeled', 'sterility'). Supports multi-word queries ranked by relevance. Filter by classification, product_type, or date range. Related: fda_search_enforcement (search by company name, classification, status), fda_recall_facility_trace (trace a recall to its manufacturing facility).
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  • Return the full structured dossier for a named entity — the canonical citable artifact for any actor, organization, ordinance, or project the corpus references. Returns: voxel_lead (134-167 word voxel-disciplined identity prose), canonical_role, the class-specific cluster (person.voting_record for board members; organization.type + jurisdiction; legislation.legal_status + effective_date + sunset_date + citation; creative_work.work_type + status + case_number), the bidirectional graph references (appears_in_meetings, appears_in_briefs, appears_in_watches, exhibits_patterns, related_entities, related_places, related_corridors), the provenance_chain, and the canonical surfaces (dossier URL, schema_id, decoder_index_hub). Each schema_id (`/entities/{slug}#{class.toLowerCase()}`) is the stable cross-page Schema.org reference — Person / Organization / Legislation / CreativeWork — that AI agents resolve to when citing the entity. Use when grounding a citation, when reasoning about an entity's full role across the corpus, or when traversing the entity graph from a single name.
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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 information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.
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