estonian

Run full morphological analysis on Estonian text. For each word returns lemma(s), part-of-speech, grammatical form, root, ending, clitic, compound parts, ambiguity info, and a usage note flagging archaic / foreign / abbreviation / interjection / proper-noun cases. By default returns the first (most likely) analysis per word; set `all_analyses=True` to return every ambiguous analysis. Each word's response includes: - lemma, partofspeech, form, root, ending, clitic, root_tokens - analyses_count: how many alternative analyses Vabamorf produced for this surface form (>1 means the word is morphologically ambiguous) - is_ambiguous: shorthand for analyses_count > 1 - usage_note: machine code (None if neutral) — "archaic" / "foreign" / "abbreviation" / "interjection" / "proper-noun" - usage_note_estonian: human-readable Estonian rendering of the same flag (quote this verbatim in Estonian replies; do NOT translate the English usage_note yourself) - indeclinable: True for words that stay in base form when used attributively (lexical indeclinables like `täis`, -tud/-nud past participles like `tuntud`, and the -mata form like `täitmata`) — i.e. they do NOT take the noun's case ending in agreement. Use this before inflecting a noun phrase so you don't wrongly decline an invariant adjective. Input is capped at 100,000 characters.

Heuristic register classifier for Estonian (formal vs colloquial). Returns a tier label (English in `tier`, correct Estonian in `tier_estonian` — quote that field verbatim when composing an Estonian-language reply rather than translating `tier` yourself, to avoid mistranslations like "formalne" instead of the correct "formaalne"), a normalised score in [-1, 1] (positive = formal, negative = colloquial), and the matched formal/colloquial markers found in the text. Useful for sanity-checking that marketing copy hasn't drifted into officialese, or that a contract draft hasn't slipped into chat tone. The lexicon covers legal-administrative AND academic/report vocabulary. `structure` adds two syntactic signals — umbisikuline tegumood ratio and noun/verb density — bounded at +0.4, applied only from 25 words up and only when the lexicon is not net-colloquial. LIMITATION: still a heuristic, not a trained model. Address forms and finer syntax go uncaught, and most newsletter prose scores 'neutral'. Use the result as a directional hint, not a verdict; for a full kantseliit breakdown with per-issue suggestions, call check_officialese. Input capped at 100,000 characters.

Find Estonian words semantically similar to the input via fastText. Returns the top-n nearest neighbours by cosine similarity over a pre-trained Estonian fastText model (Common Crawl + Wikipedia, 2018). Useful for breaking repetition, finding alternative phrasings, or expanding vocabulary when WordNet's exact-meaning synonyms aren't enough. Distinct from `synonyms`: that one returns WordNet synsets — words with the same meaning. This one returns words that *pattern* with the input in real Estonian text, which can include near-synonyms, related concepts, and (sometimes) antonyms. Known quirks of the embedding model: - **Inflections crowd the top results** for some words. fastText sees `kasutama` and `kasutada` as related because the surface forms share subword n-grams; you may want to lemmatize matches yourself to dedupe. - **Antonyms can appear** because antonyms occur in similar contexts (`tark` may surface `loll`). Treat the list as "semantically nearby" rather than "synonymous." - **Polysemy is not disambiguated.** `lahe` (which means both "bay" and the colloquial "cool") will return whichever sense dominates the training data. Single-word input only, capped at 200 characters.

Return lemma (dictionary form) for each word in the text. Concise output: `[{"word": ..., "lemma": ...}, ...]`. Input is capped at 100,000 characters.

Extract named entities (PER/LOC/ORG) using EstNLTK's CRF model. Returns `[{"text": ..., "type": ..., "start": ..., "end": ...}, ...]`. Input is capped at 100,000 characters.

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Return part-of-speech tag for each word. POS tag set: S=noun, V=verb, A=adj, P=pron, D=adv, K=adp, J=conj, N=numeral, I=interj, Y=abbrev, X=foreign, Z=punct, etc. Input is capped at 100,000 characters.

Check Estonian spelling for each word and optionally return suggestions. Returns one entry per word with `text`, `spelling` (bool), and `suggestions` (list of correction candidates) when `suggestions=True`. Input is capped at 100,000 characters. CAVEAT: Vabamorf accepts ANY morphologically well-formed word, including compounds you just invented (e.g. `toortõlkeoht`) — it splits them into valid roots and reports `spelling: true`. So passing spell_check does NOT mean a word is real, attested Estonian. For a compound you coined yourself, ask `check_compound_familiarity` whether the lemma is attested in the corpus vocabulary or Estonian WordNet.

Split a single Estonian word into syllables with quantity and accent. Each syllable entry: `{"syllable": str, "quantity": int, "accent": int}`. Input is capped at 200 characters and must contain no whitespace.

Look up Estonian synonyms via WordNet. Returns synsets (groups of synonymous lemmas) for the input word, each with its definition and example usages. Useful when you want Claude to pick a different word with the same meaning, e.g. swap an over-used verb in marketing copy. Word-sense ambiguity is preserved: a polysemous word returns multiple synsets, one per meaning. Input capped at 200 characters. WORD-FIT CHECK: when the question is "is this the right word here?" rather than "give me an alternative", READ EACH `definition` and test it against the user's actual context — do not just harvest `lemmas`. Estonian glosses routinely carry a domain constraint that decides the answer: `korpus` returns the sense "kirjaliku või suulise teksti elektrooniline kogu", so calling a set of IMAGES a `korpus` is wrong however natural it sounds in ML jargon; `andmestik` carries no such constraint. A gloss naming a medium, field, or material is a constraint on where the word may be used. Note also that a word can be well-formed, correctly spelled and still the wrong register — for that, check_officialese and classify_register, not this tool.