Olaverse Lab MCP Server

Answer typed questions about a message, ticket, record or form, with a calibrated probability for each answer. state: the thing to decide about: a string (e.g. a customer message) or a JSON object (a record). questions: {question_id: {...}}, each one of: {"type": "noul", "instructions": "The customer is asking for a refund."} -> probability it's true {"type": "choice", "instructions": "Which team?", "criteria": {"billing": "payments", "delivery": "orders"}} {"type": "score", "instructions": "How urgent?", "criteria": ["can wait", "today", "immediately"]} Works in 19 languages, including English, Pidgin, Hausa, Yorùbá, Igbo, Swahili and Arabic.

Identify the language of a text among 608 languages (built African-first; good on very short text). mode: "traffic" (user messages and chat; favours languages people actually use) or "coverage" (all languages equal, for building corpora). Returns the ISO 639-3 code, script, name, confidence and runners-up.

Turn a document into JSON shaped like `template`, with a calibrated confidence (0-1) for every field. template: the shape of the answer; each leaf names a type: "verbatim-string" (copied exactly), "string", "number", "integer", "date" (YYYY-MM-DD), "date-time", "boolean", ["a", "b"] (pick one), [["a", "b"]] (pick any), ["string"] (a list), or [{...}] (a list of objects, e.g. line items). Example: {"merchant": "verbatim-string", "total": "number", "date": "date", "items": [{"name": "verbatim-string", "price": "number"}]} Give the document as exactly one of: text: the document as text. image: a JPEG or PNG, base64 or a data URL (a photo, scan or screenshot of one page). pdf: a PDF with a text layer, base64. file_path: a JPEG/PNG, PDF or text file on this machine (local server only). instructions: optional guidance, e.g. "dates are day/month/year" or "amounts in kobo". Long documents (over 60,000 characters) are split into parts, extracted in parallel and merged; that can take minutes. Missing values come back as null. Treat fields with confidence below about 0.95 as needing a check.

The result of a long-document extraction job that extract_document left running (by its id, exj_...).

Write n natural, search-style questions that a passage answers, in the passage's own language (25 languages). Useful for FAQs, study cards and search or RAG training data. passage: up to 8,000 characters.

Put back diacritics, tone marks or Arabic vowel marks in text typed without them. language: yor (Yorùbá), ibo (Igbo), hau (Hausa), vie, pol, tur, por, spa, fra, ita or ara (Arabic). Example: "se eranko naa si gbo o?" (yor) -> "ṣé ẹranko náà sì gbọ́ ọ?"