Databox is an AI-powered business intelligence and analytics platform for teams that need clear, trusted answers fast. It offers the best of BI, without the complicated setup, steep price, or long learning curve. It provides a blend of powerful, but easy-to-use, features, from preparing datasets and creating the custom metrics your company needs to track, to building beautiful dashboards, customizing your reporting, and receiving AI-powered insights.
This is the 6th launch from Databox. View more
Databox MCP
Launching today
Databox MCP connects your business data to Claude, ChatGPT, Cursor, and n8n. Ask about revenue, campaigns, or pipeline in plain language and get answers grounded in your real metrics and business context.
Free Options
Launch Team / Built With
The MCP angle makes sense for analytics because the useful part is not just querying charts, it is keeping answers tied to the same metric definitions the team already trusts. I’d be curious how you handle permissions when Claude or Cursor asks for data across multiple teams.
I've tried way too many analytics tools that looked like they required a data degree just to set up a dashboard. The fact that Databox MCP actually gives answers without spending a week configuring things is a big time saver. Congrats on the launch!
Databox
@olga_kargopolova that's exactly what we were going for - connect your data sources once and start asking questions, no SQL or configuration rabbit holes. Thanks for the kind words and glad it landed that way!
The speed of updates from DataBox team is inspiring. I see something fresh is shipped every month on PH from DataBox. Congrats Ziga and team!
Databox
@zerotox Thank you!
Databox
The setup experience is worth calling out. Connecting Databox MCP to Claude or n8n takes under a minute -> paste the server URL, authenticate with OAuth, and your metrics are immediately accessible. No infrastructure to configure or pipelines to build. That low barrier is important because the hardest part of most analytics integrations is getting started. Removing that friction means we can go from zero to asking real performance questions in a single session.
Databox
@rtadej this is 100% true. we need to talk about this more.
Databox
What makes Databox MCP technically solid is the design of the tool layer. You get a full lifecycle interface: load_metric_data for querying with date ranges and dimension breakdowns, ask_genie for natural language analysis, ingest_data for pushing records in, and get_current_datetime to resolve relative expressions like 'last week' accurately. Each tool does one thing cleanly. The result is an AI agent that can answer performance questions with the same reliability as a well-built dashboard query - and can do it in a conversation.
Databox
The scenario I see most often is a team that has good data in Databox but spends too much time retrieving and formatting it for reporting. Databox MCP shifts that entirely. Instead of opening dashboards and exporting data, you ask a question and get an answer - in the AI tool you are already using, backed by the same data your reports use. The time savings are real, but the bigger change is that analysis becomes something anyone can do, not just the person who knows where everything lives.