DataRaum
Give your agent pre-computed dataset and column metadata so analytics questions need less raw SQL spelunking and fewer wrong joins.
Overview
io.github.dataraum/dataraum is a Grow-phase MCP server that supplies pre-computed metadata context for AI-driven data analytics over your workspaces.
What is this MCP server?
- Pre-computed metadata context engine aimed at AI-driven analytics workflows
- PyPI package dataraum 0.2.1 runnable via uvx stdio MCP transport
- ANTHROPIC_API_KEY required for LLM-powered semantic analysis over metadata
- DATARAUM_HOME configures root for workspaces, sessions, and exports
- Open-source GitHub repo dataraum/dataraum
- Published MCP server version 0.2.1
- PyPI identifier dataraum with stdio transport
- ANTHROPIC_API_KEY marked required in server manifest
Community signal: 3 GitHub stars.
What problem does it solve?
Your agent keeps misinterpreting columns and joins because warehouse metadata is thin, stale, or buried outside the chat context.
Who is it for?
Solo builders with an existing warehouse or analytics stack who want cheaper, safer agent analytics sessions.
Skip if: Teams with no Anthropic key budget or no appetite to maintain DATARAUM_HOME workspaces and exports.
What do I get? / Deliverables
After registration, queries and plans can lean on DataRaum’s semantic metadata layer and exported session context instead of rediscovering schema every time.
- Agent-addressable pre-computed metadata context for datasets
- Workspace-scoped sessions and exports under DATARAUM_HOME
- Semantic-enriched analytics grounding for downstream agent queries
Recommended MCP Servers
Journey fit
How it compares
Metadata context engine MCP, not a replacement for dbt, Airbyte, or a live SQL execution MCP.
Common Questions / FAQ
Who is io.github.dataraum/dataraum for?
Indie builders and analytics-minded solo founders who use AI agents to explore metrics and need grounded table and column context.
When should I use io.github.dataraum/dataraum?
Use it in Grow when you are iterating on dashboards, funnels, or ad hoc analysis and want the agent to reuse pre-computed semantic metadata.
How do I add io.github.dataraum/dataraum to my agent?
Run the dataraum PyPI package via uvx as stdio MCP, set ANTHROPIC_API_KEY and DATARAUM_HOME, then add the server block to Claude Code, Cursor, or Codex MCP settings.