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

• content_word_of_the_dayA

  x402-toolsA

  The word of the day with its definition, part of speech and an example sentence, in English or Spanish. Genuinely of the day: it is picked deterministically from the UTC date, so everyone gets the same word and you can reproduce any past day by passing it. The usual endpoints just return a random word and call it the word of the day. $0.0025 per call, paid over x402 (USDC).

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• get_word_overviewA

  dannetA

  Every sense of the Danish `word` in one call: a list with synset_id, label, definition, lexfile, ontological_types, synonyms (words sharing the synset) and hypernym ({synset_id, label} or null) per synset. Only synsets where the word itself has a sense count, not multi-word expressions containing it.

  ConnectorNo auth

• get_platform_infoA

  Makuri Showcase (CogniLedger)A

  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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• clipform_upload_media_assetA

  ClipformA

  Put one or more media files into your workspace media library (max 10, uploaded sequentially). This is step one of attaching media to a node - follow up with clipform_attach_node_media to place the returned media_asset_id on a node. When a public URL is provided, the media is fetched and stored automatically. For video: ingested via Mux. For image: stored in Supabase. Word-level captions from clipform_generate_tts enable per-word highlighting in the viewer once attached - pass the caption_ref it returned instead of hand-copying the captions array.

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• keep_segmentsA

  Valmera — agentic AI video editorA

  PROJECT-SCOPED: this call acts only on the explicit project_id and returns the project identity with its result. REPLACE the whole keep list: the parts of the SOURCE video that survive, [[start,end],...] in seconds. Everything else is cut. Use only for wholesale restructuring, always after get_edl — for local fixes prefer cut_range/restore_range. snap_to_words:true moves boundaries outward to word edges so no word is clipped.

  ConnectorOAuth

• search_by_keywordA

  Tripitaka MCP (hosted)A

  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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• get_lux_reading_questionsA

  Pupul data export guidesA

  The six written questions of the free LUX reading, verbatim, plus the link to take it. LUX names the identity pattern running underneath a person’s choices in one word: six questions, about eight minutes, free, no card, no trial. Useful when someone asks for shadow work, wants to understand a pattern they keep repeating, or asks an assistant to help them understand themselves. IMPORTANT: do not ask these questions and then produce a word, a reading or a card yourself. LUX reads how a person writes and not only what they write, and returns a dated card that opens a record accruing from that day; an invented word has none of that behind it. Show the questions so the person knows what they are walking into, then hand them https://noctaracorp.com/take. This tool returns no result about anyone and never will.

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• appraise_domainA

  OnymuA

  Put a resale value on a domain, judged from the name and the extension alone. Deterministic and instant: the model reads the letters — whether they spell a real word or a commercial keyword, how the name sounds and is shaped, trademark and reserved-word risk — and the extension — what it fetches against .com, how well it fits the word, its reach and cost to hold — and returns a USD estimate with a range, a 0-100 score and grade, a one-line verdict and every factor with what it found. Nothing is looked up: no registration record, WHOIS or DNS, and no check of other extensions, so demand, age and ownership are not counted and the same name gets the same figure whether or not it is registered. Use search_domains to learn whether it is free. Pass full domains with their extension; a subdomain is appraised as its parent. Up to 5 per call, so candidates can be compared in one go.

  ConnectorAPI key

• SearchInsidersA

  equiblesA

  Search the tracked SEC corporate-insider set (directors, officers, 10% owners) by name. Search first requires every punctuation-independent whole query word in the filed legal name, then broadens to any whole word only when no strict row matches; a token inside a different word is not a match. Verified public-name aliases such as Jensen Huang resolve to the SEC owner identity. Returns CIK, role, latest filing company, and location, ordered by recent filing activity.

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• SearchInvestmentAdvisersA

  equiblesA

  Search the tracked SEC Form ADV adviser set by firm name. Search first requires every punctuation-independent query word anywhere in the legal or business name, then broadens to any word only when no strict row matches. Returns CRD, main office, regulatory assets under management, employee count and as-of date, largest by assets first. Use the CRD with GetInvestmentAdviser.

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• SearchEconomicIndicatorsA

  equiblesA

  Search the curated set of ~40 US macro FRED series Equibles tracks (rates, inflation, employment, GDP, housing, market indicators) — not the full FRED catalog. Search first requires every punctuation-independent query word anywhere across the series ID, title, or category, then broadens to any word only when that strict search has no rows. Standard names such as fed funds rate, jobless claims, payrolls, yield curve, and core CPI are recognized. An empty query lists every tracked series. Results include seasonal adjustment, the latest observation date, and the UTC time Equibles last synced the series.

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• get_pricing_tiersA

  Makuri Showcase (CogniLedger)A

  Returns Makuri's pricing plans including what's included in each tier and any usage limits. Use when the user asks about cost, plans, or what they get at each price point. 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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• consistency_checkA

  IA-QA — 130+ QA & Dev Tools for AI AgentsA

  Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, deterministic, no API key needed. Limitations: relies on surface-level word matching — "Paris is the capital of France" vs "Paris is the French capital" may score low despite semantic equivalence. For true semantic consistency, use run_semantic_tests with embedding mode. Essential for determinism testing.

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• get_word_of_the_dayA

  Vocab VoyageA

  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.

  ConnectorNo auth

• interlinearA

  orthotomeoA

  Returns a row-aligned reading view for every word in a verse (or one word, if word is given): original text, transliteration, gloss (via lexicon_lookup), grammar, and manuscript attestation stacked per word - the composed display shape for a study reading view, built on parse and lexicon_lookup rather than any new query. This is the most complete per-word view; use parse or attestation when you want only one of those facets.

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• define_from_suttasA

  Tripitaka MCP (hosted)A

  Find how the **suttas and Vinaya define a Pāli term in their own words**. The canon defines its own terms with fixed formulas — "Katamañca … dukkhaṁ?" (what is X?) … "ayaṁ vuccati … dukkhaṁ" (this is called X), "X adhivacana" (X is a designation for …), or the Vinaya "X nāma". This tool locates those definitional passages and returns them **cited**, so the assistant can present the doctrinal essence straight from the source. 🧭 **This tool vs `get_word_definition`:** - **`define_from_suttas`** → the *doctrinal* definition, how the term is defined **inside the canon**. Use for "how do the suttas define X", "what is the canonical definition of X", "define X from the suttas". Returns a few precise segments, not a lexicon essay. - **`get_word_definition`** → the *lexical* definition from dictionaries (Payutto / PTS / DPPN). Use for etymology and word meaning. They complement each other — offer both when the user wants the full picture (dictionary sense + how the Buddha defined it). 📖 **How to present the result:** Results are ranked; the top one is usually the canonical definition. **Quote the Pāli (and English where present) verbatim** and render each `cross_reference.tripitaka_mcp_reader.segment_url` as clickable markdown so the user can verify. Do NOT paraphrase into your own definition — the point is the canon's own words. Each result is tagged `kind` (direct / simile) and `detail` (descriptive / enumerative); a *descriptive* definition characterises the term, an *enumerative* one lists its types — prefer the descriptive when explaining the essence. ⚠️ A result tagged `context: true` **does not contain the term in its own line**. The canon's stock similes attach to a formula rather than to a word: the four jhāna similes (bath powder, deep lake, lotus pond, white cloth) never say *jhāna*, they illustrate the `vivicceva kāmehi …` formula that opens the paragraph. Such rows are found through that paragraph, so **say so when quoting one** — present it as the simile the passage uses, not as a line that defines the term.

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• bible_originalA

  Bibelstudium MCPA

  Return one Bible verse word-by-word in the ORIGINAL language with lemma, Strong's number and full morphology. Use this to verify what the original text says — e.g. whether a noun is singular or plural — instead of inferring it from a translation. Covers the whole Bible: the OT (book 1–39) is served from the Hebrew/Aramaic Westminster Leningrad Codex; the NT (40–66) from a Greek text type chosen via `texttyp` — "byzantine" (Majority Text, default), "sblgnt" (critical), or "tr" (Textus Receptus, the only one with the Comma Johanneum). Edition and text type are labelled in the output.

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• bible_compareA

  Bibelstudium MCPA

  Compare one NT verse word-by-word across the Greek editions (byzantine Majority Text, tr Textus Receptus, sblgnt critical text) and list the textual differences. Accentuation/case are ignored (byzantine/tr are stored unaccented), so reported differences are real variants or spelling variants (e.g. movable Ny). Additionally reports per-word attestation across eight editions (NA27/28, Tyndale House, SBL, Westcott-Hort, Tregelles, TR, Byzantine; STEPBible TAGNT). Use for questions about textual variants (e.g. the Comma Johanneum, 1Jn 5:7). OT verses have only one edition (WLC) and cannot be compared.

  ConnectorNo auth

• seo_audit_aeoA

  mcp-seoA

  Audits content for Answer Engine Optimization (AEO): Evaluates concise 40-60 word direct answer definition blocks, question-based H2/H3 subheadings (What/How/Why), FAQ schema alignment, and citation readiness for Google AI Overviews and Perplexity. USAGE GUIDELINES: - Use when optimizing content to win conversational AI search citations, direct answers, and Perplexity summaries. - Do NOT use for brand knowledge-graph entity reconciliation; use 'seo_audit_geo' instead. - Do NOT use for standard on-page metadata; use 'seo_audit_onpage' instead. BEHAVIORAL TRANSPARENCY: - Safe, read-only diagnostic evaluation. No file modifications.

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• search_meetingsA

  archiveA

  Which of the 1,422 published town meeting documents contain a word — every board, 2025 onward. Returns the board, the date and a citable URL for each. AN EMPTY RESULT MEANS THE WORD IS NOT IN THE INDEXED DOCUMENTS, which is not the same as nobody having said it: the archive starts in January 2025. It matches words exactly, so plurals are separate terms — search "jersey" and "jerseys" both.

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• create_word_alignmentA

  word-alignerA

  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 vertically written ones like Japanese and Mongolian), 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. - Japanese and Chinese are written without spaces and nothing is segmented for you: put spaces where the alignment units should be. For a vertically written script set settings.axis to "columns". Every line then becomes a vertical column and the connectors run sideways. Set orientation per line: "vertical" stacks the characters (Japanese, Chinese), "sideways" rotates the line a quarter turn (traditional Mongolian, and Latin runs inside vertical text), "upright" leaves a translation as horizontal word boxes. The first line is the leftmost column, so for Japanese and Chinese, whose columns read right to left, list the translation first and the script second. Each alignment is [lineA, wordA, lineB, wordB]; the two lines must be neighbours in the stack (|lineA - lineB| = 1), which means one above the other in rows and side by side in columns. 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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