DataRaum
概览
作者提供的说明(英文原文)
Pre-computed metadata context engine for AI-driven data analytics
PopNx MCP 的三大支柱
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
- 用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
- 我们的基础检查i1 项中通过 1 项依据公开信息检查;尚未扫描代码
- 使用情况i非常新★ –
它能帮你做什么
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
- 连接方式: 在你的电脑上运行
- 传输方式: stdio
- 分类: 营销与社交媒体
- 你需要填写:ANTHROPIC_API_KEY
安装
选择你使用的 AI 应用,然后复制设置。
安装之前
- 基础检查:1 项中通过 1 项
- 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
- 需要你自己填写:
ANTHROPIC_API_KEY,DATARAUM_HOME
标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
你需要
- PyPI已安装 Python 的 uv(用于 uvx 命令)
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"dataraum": {
"command": "uvx",
"args": [
"dataraum"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"DATARAUM_HOME": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add dataraum --env ANTHROPIC_API_KEY=your-value --env DATARAUM_HOME=your-value -- uvx dataraum运行前请先填写空着的值。
或者自己复制设置
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"dataraum": {
"command": "uvx",
"args": [
"dataraum"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"DATARAUM_HOME": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
或者自己复制设置
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"dataraum": {
"type": "stdio",
"command": "uvx",
"args": [
"dataraum"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"DATARAUM_HOME": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
需要你填写的值
- Anthropic API key for LLM-powered semantic analysis
DATARAUM_HOMEenvRoot directory for workspaces, sessions, and exports
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它能做什么
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
基础检查i
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日
- ✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 dataraum 验证,该账户拥有此仓库
- … 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
- … 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
- – 不适用
端点有响应
它在你的电脑上运行,没有互联网端点
- – 不适用
使用 OAuth 登录
在你的电脑上运行,改用本地设置
- … 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与“营销与社交媒体”中的其他服务器比较i
- 星标数多于此分类中其他 3611 个服务器的 0%
- 发布到注册中心的时间比此分类中其他 3611 个服务器的 6% 更新
- 基础检查结果等于或优于此分类中其他 3611 个服务器的 100%
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
常见问题
DataRaum 安全吗?
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
它需要哪些密钥或代码?
你需要填写:ANTHROPIC_API_KEY
它在我的电脑上运行还是在云端?
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
它还在维护吗?
GitHub 仓库最后更新于–,最新版本(0.2.1)于7个月前进入注册中心。
哪些应用可以使用它?
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。
有什么可以替代它?
同一分类中有:MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
替代选择
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Google Analytics MCP
GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.
BibiGPT YouTube Video Summarizer
AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.
作者的 README
打开 README
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
DataRaum
The understanding layer that grounds an organization's operating model in its own data.
A semantic layer tells BI tools what columns are *called*. DataRaum learns what they *mean* — the concepts, relationships, rules, and measures of the organization — and grounds each one in the actual data, with a measured confidence behind it. See the docs for the full picture.
Monorepo layout
packages/
├── engine/ # Python — pipeline, detectors, Temporal activity worker ├── cockpit/ # TypeScript — TanStack Start web UI ├── dataraum-config/ # YAML data — entropy config, LLM prompts, verticals (bind-mounted, never imported) └── infra/ # docker-compose orchestration
Each package has its own README. Start there if you're working in a specific package.
Status
DataRaum runs as a multi-container platform, isolated per workspace:
- engine (Python) — a Temporal activity worker, no HTTP. Does the durable analysis (
add_source,begin_session,operating_model) and writes metadata to the workspace's Postgres schema. - cockpit (TanStack Start) — the web app you use. Hosts the chat agent, renders the results, and orchestrates the journey by triggering engine workflows via Temporal.
Each workspace runs its own pair of those two containers. In front of them sit the portal (the cockpit image in a second role — login, membership routing, workspace provisioning) and Caddy, which serves the portal on the parent domain and each workspace on its own subdomain.
They share one substrate: Postgres (metadata + cockpit state + catalogs), an S3 object store (the DuckLake data lake + uploads), and Temporal (durable orchestration). No HTTP seam between engine and cockpit — the integration surface is Postgres + Temporal. See the platform architecture.
Quick start
# Set the LLM key
cp packages/infra/.env.example packages/infra/.env echo "ANTHROPIC_API_KEY=sk-ant-..." >> packages/infra/.env
Bring up the whole installation (substrate, engine worker + cockpit for the
default workspace, portal, and the Caddy ingress)
docker compose -f packages/infra/docker-compose.yml up -d --wait
Engine health = the Temporal worker heartbeat (no HTTP endpoint):
docker compose -f packages/infra/docker-compose.yml run --rm --no-deps \ --entrypoint temporal temporal-admin-tools \ worker list --namespace default --address temporal:7233 # → Status: Running
Sign in at the portal, then open the default workspace from there
open http://dataraum.localhost # dev@dataraum.dev / dataraum-dev
Caddy routes by hostname: the parent domain serves the portal (login + your workspaces), and each workspace has its own subdomain (http://ws1.dataraum.localhost). localhost:3000 is published for debugging only — the session cookie is scoped to the parent domain, so a browser there is redirected to the portal and a script gets 401.
The thing that bites on a first run: Caddy binds port 80. If something already holds it (macOS ships Apache), up fails at container start and the portal never comes up — set CADDY_HTTP_PORT *and* a matching DATARAUM_PORTAL_ORIGIN to move it. (*.localhost needs no /etc/hosts entry.)
Compose defines exactly one workspace pair — bootstrap scaffolding, so a fresh install has something to log into and something for the provisioner to clone. Every other workspace is created from the portal (New workspace) or bun run workspace:create; compose does not grow a service per workspace.
Full walkthrough, including troubleshooting: Running the stack.
For UI iteration, run the cockpit dev server outside docker for hot reload — see packages/cockpit/README.md.
Run a released version (published images)
The quick start above builds the engine and cockpit from source. To run the published release images instead — a deploy host, no build toolchain — layer the release overlay and name the version:
export DATARAUM_VERSION=1.2.3 # any tag from a GitHub Release
docker compose \ -f packages/infra/docker-compose.yml -f packages/infra/docker-compose.release.yml \ --env-file packages/infra/.env up -d --wait --no-build
This pulls ghcr.io/dataraum/{dataraum, dataraum-cockpit, dataraum-cockpit-migrate} at that tag. See Deployment for the images, schema/migration handling, and the per-workspace topology.
Develop
- Engine (Python):
cd packages/engine && uv sync --group dev && uv run pytest --testmon tests/unit -q. Seepackages/engine/README.mdandpackages/engine/CLAUDE.md. - Cockpit (TypeScript):
cd packages/cockpit && bun install && bun --bun run dev(the--bunflag is required). Seepackages/cockpit/README.mdandpackages/cockpit/CLAUDE.md. - Pull the engine metadata schema (cockpit):
cd packages/cockpit && DATARAUM_WORKSPACE_ID= METADATA_DATABASE_URL= bun run db:pull:metadata. Re-run after the engine adds/changes SQLAlchemy models.
Documentation
Platform docs live in docs/ (workspace root) and are published via Zensical. Start at docs/index.md, or serve the site locally:
uv run --project packages/engine zensical serve # run from the repo root
- Documentation home — what DataRaum does, at a glance
- The journey · pipeline & phases · learnable surface · measurement & detectors
- Platform architecture — under the hood
- Decision records — the *why* behind the architecture
License
Apache 2.0 — see LICENSE.