io.github.Olaverse-Labs/olaverse-mcp · v0.2.1 · productivity
Olaverse Lab is healthy: it completed an MCP handshake in 983 ms and exposes 6 tools.
Document extraction with calibrated confidence, decisions, language ID in 608 languages, diacritics.
Endpoint: https://mcp.olaverselab.com/mcp (streamable-http)
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| Users (last 30 days) | 0 |
| Handshake | Healthy |
| Auth needed | None |
| Tool count | 6 |
| Latency | 983 ms |
| Last checked (UTC) | 2026-11-07T03:30:42.728Z |
| Checked (UTC) | Status | Latency | Error |
|---|---|---|---|
| 2026-11-07T03:30:42.728Z | Healthy | 983 |
extract_document | 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 |
get_extraction_job | The result of a long-document extraction job that extract_document left running (by its id, exj_...). |
decide | 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 |
detect_language | 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 |
restore_diacritics | 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?" |
generate_questions | 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. |