DataRaum

Name: DataRaum
Author: dataraum

by dataraumversion 0.2.1i★ –iRuns on your computer

Registry nameio.github.dataraum/dataraum

Install

Overview

Description from the maker (original English)

Pre-computed metadata context engine for AI-driven data analytics

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiVery new★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Marketing & social media
You need to fill in: ANTHROPIC_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: ANTHROPIC_API_KEY, DATARAUM_HOME

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "dataraum": {
      "command": "uvx",
      "args": [
        "dataraum"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "DATARAUM_HOME": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add dataraum --env ANTHROPIC_API_KEY=your-value --env DATARAUM_HOME=your-value -- uvx dataraum

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "dataraum": {
      "command": "uvx",
      "args": [
        "dataraum"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "DATARAUM_HOME": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "dataraum": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "dataraum"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "DATARAUM_HOME": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

ANTHROPIC_API_KEY secret required env
Anthropic API key for LLM-powered semantic analysis
DATARAUM_HOME env
Root directory for workspaces, sessions, and exports

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 9, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account dataraum, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Marketing & social mediai

More stars than 0% of the other 3627 servers in this category
Published to the registry more recently than 6% of the other 3627 servers in this category
Basic checks equal to or better than 100% of the other 3627 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is DataRaum safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: ANTHROPIC_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.1) reached the registry 7 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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. See packages/engine/README.md and packages/engine/CLAUDE.md.
Cockpit (TypeScript): cd packages/cockpit && bun install && bun --bun run dev (the --bun flag is required). See packages/cockpit/README.md and packages/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.

DataRaum

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Marketing & social mediai

Common questions

Alternatives

MCP YouTube Transcript

Google Analytics MCP

BibiGPT YouTube Video Summarizer

README from the maker

DataRaum

Monorepo layout

Status

Quick start

Bring up the whole installation (substrate, engine worker + cockpit for the

default workspace, portal, and the Caddy ingress)

Engine health = the Temporal worker heartbeat (no HTTP endpoint):

Sign in at the portal, then open the default workspace from there

Run a released version (published images)

Develop

Documentation

License