How many steps does it take to answer a simple data question?
We've been thinking a lot about this moment. A stakeholder asks "how did our paid campaigns do last week?" or "are we on track for the month?" - and even with dashboards already built, getting a clean answer still takes more steps than it should.
You open the right tool. Filter the right date range. Cross-reference another source. Paste it into a Slack message. Sometimes you just end up saying "let me get back to you on that."
The data exists. The dashboards exist. But the path from question to answer still has too much friction in it.
That's the problem we set out to solve with Databox MCP - launching on Product Hunt May 28. Instead of navigating dashboards, you ask the question directly in whatever AI tool you already use - Claude, ChatGPT, n8n, Cursor - and get an answer pulled from your live data.
We'd love to hear from you before we launch:
What does that moment actually look like for you?
Is it the scrambling to find the right view?
The back-and-forth with someone who owns the dashboard?
Or something else entirely?
Replies
Databox
@olivier_jury Olivier, this is exactly the scenario we built for - thanks for laying it out so clearly.
On your three points:
1. Charts + numbers in 10s - yes, that's the goal. Inline chart rendering (Widgets) is shipping with our May 28 launch, so you'll get a visual directly in the AI client alongside the raw numbers. Raw numbers and analysis are already live today.
2. Setup is under 2 minutes - OAuth flow, no IT, no permissions config required. If you have a Databox account, you're in.
3. Granular access control - this one we want to be honest about. Right now, MCP inherits your existing Databox permissions, and you can scope separate API keys to specific data sources. Metric-level restrictions (CAC/ROAS but not revenue) aren't a native MCP feature yet - but it's exactly the kind of feedback shaping our roadmap.
No beta waitlist - Databox MCP is live now at databox.com/mcp. We're doing a proper PH launch on May 28, but you can connect today and test it before then. Would love your take on whether #1 and #2 land the way you described.
Databox
@olivier_jury they are currently view-only, but the AI client can either provide you with exportable data or vice versa, the data is exportable from Databox.
The overlooked step, in my experience, is not finding the number — it’s turning the number into an answer someone can safely act on.
For a stakeholder question like “are paid campaigns on track?”, I’d want the MCP answer to carry a tiny receipt: date range used, source/dashboard pulled from, filters applied, comparison baseline, and any confidence caveat. Then the Slack-ready answer isn’t just faster; it’s reviewable. Otherwise the time saved on dashboard navigation can come back as time spent asking “wait, what exactly did this include?”
Databox
@jim_jeffers Jim, this is a sharper version of the problem than most people articulate - and it's exactly why grounding matters more than speed.
With Databox MCP, the answer isn't generated from general knowledge - it's pulled from specific named metrics in your Databox account, with their definitions, connected data sources, and time ranges attached. So when Claude says "paid campaigns are down 12% vs last week", it can show you which metric it queried, from which integration, over which date range. The receipt is there.
What's automatic: the data source, metric name, and time range used.
What requires a good prompt (or a saved instruction): the confidence caveat and explicit comparison baseline framing.
Your point on the comparison baseline is the one we want to take back to the team. Right now the AI will use whatever range you ask for, but surfacing the baseline as a visible part of the answer - not just something the AI knows - is a real gap. That's the difference between a fast answer and a safe one.