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255,062 tools. Last updated 2026-07-03 15:37

"Definition and meaning of the word 'word'" matching MCP tools:

  • 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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  • 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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  • 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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  • Use this when the user asks what a specific word means, requests its definition, part of speech, synonyms/antonyms, or an example sentence. Returns curated dictionary data from the Vocab Voyage corpus. Do not use for sentence-level meaning disambiguation (call explain_word_in_context) or for daily word prompts (call get_word_of_the_day).
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  • Find an English word given a description of its meaning. Use when the user describes a concept but doesn't know the word. Returns words ranked by semantic similarity across 162,000 English words.
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  • Get a dictionary definition for an English word (meanings, examples, phonetics). Use for writing and language agents. Example call: {"word": "ephemeral"} Cost: $0.005–$0.05 USDC on Base per call.
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Matching MCP Servers

Matching MCP Connectors

  • Turn a phrase and its translation into a shareable word-alignment diagram.

  • Word Aligner exposes an MCP server so AI agents can turn a phrase and its translation into a shareable word-alignment diagram. The server runs over Streamable HTTP at aligner.tinygods.dev/mcp with no authentication and a single tool, `create_word_alignment`. An agent translates and tokenizes the text, works out which words correspond, calls the tool, and gets back a URL plus a preview image.

  • Advanced word search. Find words matching a combination of meaning, pronunciation, and spelling constraints.
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  • [word-intel] Get a random interesting English word with its full definition, pronunciation, examples, synonyms, and antonyms. Great for vocabulary building or word-of-the-day features.
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  • Hybrid search — combines keyword + semantic search via RRF. Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. **This is the recommended tool for "discourses about X" / concept queries**, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use `assasati/passasati/dīghaṁ` instead of `ānāpānassati`). 💡 **Hints for the AI client:** - English queries usually work best (e.g. `mindfulness of breathing`) because the embedding model is multilingual but EN-primary. - Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions). - The default `limit=5` is often too small for a topic survey — use `limit=15-20` (max 20) for good coverage. - Ranking is by similarity, NOT canonical importance — locus classicus suttas (e.g. MN118, DN22) may rank below smaller suttas that happen to use the exact vocabulary. Treat results as a starting point, then call `get_sutta` for the canonical references.
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  • Create a shareable Word Aligner diagram that shows which words match across two or more stacked lines of text (a translation and its source, an interlinear gloss, IPA, etc.). Returns a URL that opens the interactive diagram, plus a preview image. Use this when the user wants to translate a phrase and show word correspondences, align a translation with its source (including RTL scripts like Hebrew or Arabic), or build a Leipzig-style interlinear gloss. Word indices are 0-based token positions. Tokenize each line the same way the tool does before assigning indices: - Whitespace always splits ("I have been going" -> I[0] have[1] been[2] going[3]). - The characters in settings.tokenSplitChars (default ".-|") also split and are then removed from the rendered text, so "go.PST.IPFV" becomes three tokens (go, PST, IPFV) and the dots disappear. For Leipzig glosses set tokenSplitChars to "-|" to keep the dots. - Punctuation stays attached by default ("Hello, world!" -> Hello,[0] world![1]). - In RTL lines, word 0 is the logically first word (rightmost on screen); index in reading order. Each alignment is [lineA, wordA, lineB, wordB]; the two lines must be vertically adjacent (|lineA - lineB| = 1). To express many-to-one, list each target word as its own tuple. Tokens that share a connection group get the same color automatically.
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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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  • 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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  • 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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  • 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 two outputs: 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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  • 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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  • 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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  • Returns the list of languages supported by Makuri, with separate coverage details for user interface versus AI tutor interactions. Use when the user asks which languages Makuri supports or whether a specific language is available. 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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  • 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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  • 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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