Olaverse Lab

io.github.Olaverse-Labs由 GitHub 账号 Olaverse-Labs 发布v0.2.1更新于 Nov 7, 2026

Document extraction with calibrated confidence, decisions, language ID in 608 languages, diacritics.

已验证Streamable HTTP可网页运行知识与记忆数据与分析AI 与机器学习

概览

AI 生成的概览

Olaverse Lab 将小型专用语言模型封装为工具：按模板把文档抽取为带置信度的 JSON、类型化判断、语言识别与变音符号还原。

可以做什么
  • 按模板把文本、JPEG/PNG 图片或 PDF 抽取为结构化 JSON，并为每个字段给出校准后的置信度；长文档以后台任务运行，稍后取回结果
  • 针对消息、工单或记录回答类型化问题（是/否、选项、评分），并给出校准概率
  • 在 608 种语言中识别文本语言，可选流量或覆盖模式
  • 为 11 种语言还原声调符号、重音符号和阿拉伯语元音符号
  • 生成某段文字所能回答的搜索式问题，使用该文字本身的语言
适用场景
适合需要从收据、表单、扫描件或 PDF 中提取字段、并依靠逐字段置信度判断可信度的场景，也适合处理英语和非洲语言文本的语言识别、变音符号还原或问题生成。
运行要求
需要一个在 Olaverse 控制台创建的 API 密钥：本地 uvx 包通过 OLAVERSE_API_KEY 传入，托管端点通过 Authorization 的 Bearer 头传入。本地运行需要 uv/uvx，托管端点无需安装。可选设置 OLAVERSE_API_URL 与 OLAVERSE_JOB_WAIT。无文本层的扫描 PDF 需按 JPEG 或 PNG 图片提交。
安装前请注意
每次调用都按你的 API 密钥计费，超出赠送额度后会产生费用。API 密钥属于机密，通过 OLAVERSE_API_KEY 或 Authorization 头传递。提交的文档、消息和文本会发送到 Olaverse 的服务器。置信度只对模型所针对的文档类型经过校准，自动化前请用自己的样本验证阈值。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Olaverse Lab，将其添加到工作区。
  2. 连接 Authorization token 凭据。它们会被私密存储，绝不会在公开页面上展示。
  3. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.Olaverse-Labs%2Folaverse-mcp/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "olaverse-lab": {
      "type": "http",
      "url": "https://mcp.olaverselab.com/mcp",
      "headers": {
        "Authorization": "Bearer <YOUR_TOKEN>"
      }
    }
  }
}

README

Olaverse MCP server

Olaverse Lab's API as tools for AI agents: Claude, Cursor and any other MCP client. The models are small, task-specific and strongest on English and African-language text.

ToolWhat it does
extract_documentA document (text, a JPEG/PNG photo or scan, or a PDF) to JSON shaped like your template, with a calibrated confidence for every field. Long documents are split, extracted in parallel and merged.
get_extraction_jobCollects a long-document result that's still running.
decideTyped questions about a message, ticket or record (yes/no, choice, score), with calibrated probabilities. 19 languages.
detect_languageThe language of a text, among 608 (built African-first, good on very short text).
restore_diacriticsPuts back tone marks, accents and Arabic vowel marks in 11 languages, including Yorùbá, Igbo, Hausa and Arabic.
generate_questionsSearch-style questions a passage answers, in the passage's own language (25 languages).

Set up

You need an Olaverse API key: create one at console.olaverselab.com (new accounts get $1 of free credit). Calls are billed to your key at the usual prices.

The server runs with uv (uvx downloads and runs it).

Claude Code

bash
claude mcp add olaverse --env OLAVERSE_API_KEY=your-key -- uvx olaverse-mcp

Claude Desktop, Cursor and others (the mcpServers block of the client's MCP config file)

json
{  "mcpServers": {    "olaverse": {      "command": "uvx",      "args": ["olaverse-mcp"],      "env": { "OLAVERSE_API_KEY": "your-key" }    }  }}

Hosted (no install). Olaverse also runs this server at https://mcp.olaverselab.com/mcp (streamable HTTP). Send your key with each request as Authorization: Bearer your-key. The hosted server takes documents as text, or as an image or pdf in base64; it doesn't read files or fetch URLs.

Use it

Ask in plain words, and the agent picks the tool:

  • "Extract the merchant, date, total and line items from ~/Downloads/receipt.jpg."
  • "Is this customer message asking for a refund, and how urgent is it? «Ẹ jọ̀ọ́, wọ́n ti yọ owó lẹ́ẹ̀mejì…»"
  • "Which language is 'Mo fẹ́ lọ sí ọjà'?"
  • "Add the tone marks back: se eranko naa si gbo o?"

Confidence. extract_document returns a confidence for every field. It's calibrated: fields at 0.95 and above are right about 95% of the time on the documents it was built for. Check a threshold on a sample of your own documents before you automate on it.

Settings

VariableDefault
OLAVERSE_API_KEYrequiredYour API key.
OLAVERSE_API_URLhttps://api.olaverselab.com
OLAVERSE_JOB_WAIT900Seconds a long-document extraction waits before handing back a job id for get_extraction_job.

Scanned PDFs (no text layer) aren't read directly: send their pages as JPEG or PNG images.

Licence

Apache-2.0.

mcp-name: io.github.Olaverse-Labs/olaverse-mcp

来源：README.md，提交 b5d4b2a

工具

6

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 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.

输入参数pdftextimagetemplate必填file_pathinstructions

get_extraction_job

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

输入参数job_id必填

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": "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.

输入参数state必填questions必填

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 and runners-up.

输入参数modetext必填top_k

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?"

输入参数text必填language必填

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.

输入参数npassage必填language

版本历史

1
  1. v0.2.1最新Nov 7, 2026

评分

已满足适用检查项的 100%

该评分衡量此收录条目的完整程度，依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价，也不表示服务器安全或当前可用：可连通性是带日期的检查结果，评分也不包含安装量或用户评价。

  • 近 30 天内可连通必需25 分
  • 已声明认证方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 图标10 分
  • 文档15 分
  • 许可证10 分