Arquestra Audit

io.github.pablo-ramossanchez由 GitHub 账号 pablo-ramossanchez 发布v0.8.1更新于 Nov 3, 2026

发布前审核数据文件：每个图表一盏灯。你的数据永远不会到达服务器。

AI 翻译由 AI 模型根据下方的原文描述翻译，可能有误。

原文描述

Audit a data file before publishing: one light per figure. Your data never reaches the server.

发布方自己的服务：audit.domains - Domain Valuation

已验证Streamable HTTP可网页运行

服务详情

标识符: io.github.pablo-ramossanchez/arquestra-audit
传输: Streamable HTTP
运行时: Web executable
信任: Verified
发布于: Nov 3, 2026

安全提示

MCP 服务会接收对话中的工具调用请求，并可能执行外部操作。启用前请先了解该服务能做什么，并只授予它所需的权限。

注册中心已验证此条目所属的 GitHub 账号 pablo-ramossanchez。这并不表明该账号就是产品方。

SourceWeft 的检查只涵盖条目是否完整、端点是否响应；不审查服务器的代码，也不审查其工具的行为。

在 README 中添加徽章

向读者展示该 MCP 服务已收录于 SourceWeft，徽章链接到本页。

[![Listed on SourceWeft](https://sourceweft.com/badge/mcp/io.github.pablo-ramossanchez%2Farquestra-audit.svg)](https://sourceweft.com/mcp/io.github.pablo-ramossanchez%2Farquestra-audit?utm_source=readme&utm_medium=badge)

概览

Arquestra Audit 的托管 MCP 服务（arquestra.dev）；无需登录；Streamable HTTP；5 个工具。

暂无 AI 概览：以下信息来自注册中心条目和服务本身。

端点: arquestra.dev
最近检查: 2026年11月3日

安装

在 SourceWeft 中

打开控制台中的 Arquestra Audit，将其添加到工作区。
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.pablo-ramossanchez%2Farquestra-audit/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": {
    "arquestra-audit": {
      "type": "http",
      "url": "https://arquestra.dev/api/mcp/audit"
    }
  }
}

README

Arquestra Audit · MCP server

Check your own data before you publish a result from it. Arquestra Audit is a remote MCP server by Arquestra that walks your AI assistant through an audit of a data file: it agrees with you how to read each column, measures the file where it is (in the chat's audit panel or with a small script on your computer), and shows one light per figure, red, amber or green, with the reasons.

Your data never reaches the server and nothing is stored. The measuring runs in your browser or on your computer; only counts come back.

Endpoint: https://arquestra.dev/api/mcp/audit (Streamable HTTP, no sign-in)
Page: arquestra.dev/assets/audit

Connect it

Claude (claude.ai, desktop, mobile): open Claude's connectors, search Arquestra Audit and press Connect. Not in the list yet? Add it directly.

Claude Code:

or install this repository as a plugin (Manage plugins → Install by GitHub URL → https://github.com/pablo-ramossanchez/arquestra-audit-mcp).

VS Code: install

Cursor: install

ChatGPT: settings → connectors (developer mode) → add https://arquestra.dev/api/mcp/audit.

Any other MCP client: add a remote server with the URL above.

Tools

Tool	What it does
`start_audit`	Proposes how to read your file (the role of each column, each result written as a claim) from its column names and the results you want to publish.
`measure_in_app`	Opens the audit panel in the chat; the panel reads your file in your browser and measures it with the confirmed reading.
`prepare_script`	Returns `measure.py`, a fixed Python script (standard library, no network) that counts what each check needs and prints counts, never rows.
`show_audit`	Shows the lights: one per figure, with the reasons and a small chart.
`example_audits`	How the audit works, with worked examples.

About Arquestra

Arquestra builds open, checkable data assets on public procurement and AI governance: the Consulting Market Radar (public consulting tenders in Europe and who wins them), the AI Procurement Observatory, research papers, and this audit.

Earlier versions of this repository held a plugin for a scientific-computing product; that product is now listed as Arquestra Chem on arquestra.dev.

来源：README.md，提交 dcde607

工具

start_audit

Start an audit of the person's data

Proposes how to read a data file for an audit, from its column names (optionally a type and one example value each) and the results the person wants to publish. Returns a proposed spec (the role of each column, each result written as a claim), how claims are written, the catalog of checks and the points the person has to confirm. It receives no rows.

输入参数columns必填figuresdataset_name

measure_in_app

Measure the person's file inside the audit panel

Opens the audit panel in the chat. Without a spec, the panel receives the person's file, reads it in their browser and returns a profile of it: per column its kind, empty cells, distinct values, the values of categories with their counts, the range of numbers and dates, and the first rows. With a confirmed spec, the panel measures that same file in the browser with measure.py and shows one light per result. The file is not sent to this server.

输入参数speccolumns

prepare_script

Get the script that measures the data

Returns measure.py with a confirmed spec written in: a fixed Python script (standard library, no network, fixed seeds) that counts what each check needs from a data file and prints those counts with its own fingerprint and no rows. Reads .csv or .json. If the spec has errors, returns the errors instead.

输入参数spec必填columns

show_audit

Show the lights of the person's audit

Turns the output of measure.py into one light per result (red, amber or green, with its reasons), after checking that the output comes from the unchanged script and from the given spec. Shows the lights in the chat and returns them, with a link to the same view on arquestra.dev that carries only codes. Stores nothing.

输入参数spec必填output必填

example_audits

What the audit is, with worked examples

Published example audits, such as the AI Project Radar, and how the audit works. With no arguments: the overview and the list of examples. With a name: one example, result by result (level filters by light). With a name and an id: one result with every reason and what to change. They are not the person's data.

输入参数idlangnamelevel