Databox MCP

Chat with your business data inside Claude, ChatGPT and more

Productivity

Analytics

Artificial Intelligence

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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.

What’s the biggest productivity gain teams usually get after connecting Databox MCP? Faster reporting, better decisions, fewer manual data checks?

I spent more than 100,000 dollars trying to build my own data warehouse before I gave up and used the Databox MCP instead.

The problem I was solving is the one nobody likes to talk about with AI and data: an LLM will give you a confident answer whether or not the data supports it. When you manage ad spend across dozens of markets for a client, a confident wrong answer is expensive.

So I built Arcanian OS on top of the Databox MCP. It runs in Claude Code, connects to live data, and every claim it makes carries a confidence score. Data straight from a CRM pipeline scores high. A number inferred across two loosely connected sources scores low and gets flagged for a human. When a question contains a contradiction, the system does something most AI tools never do. It refuses to answer and asks me to rephrase.

It runs an internal debate among agents before it reaches a conclusion, creates tasks when it spots a risk or an opportunity, then checks back later to see whether finishing the task actually moved the metric. The learnings get anonymized and reused across every client in the system.

None of this works without a data layer that pulls accurately and defines each metric the same way every time. That layer is the Databox MCP. I open-sourced the whole operating system on GitHub so other agencies can run it too.

The real challenge in analytics MCP isn't data retrieval, it's grounding the LLM in correct metric definitions. We've run into this on customer data pipelines: 'churn' means different things across systems. How does the MCP layer handle semantic disambiguation? When a user asks about revenue or pipeline, does the context layer resolve conflicting metric definitions or surface the ambiguity to the user?

Been building on the Databox MCP for months alongside the HubSpot MCP. The combination unlocks a revenue intelligence layer most HubSpot agencies haven't explored yet. Excited to see this go public today.

The semantic layer design is what separates this from copy-paste workflows. You're connecting to metrics with definitions and historical context baked in, so the AI knows if a number is actually good. Does it handle custom fiscal calendars or non-standard reporting periods?

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!

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.

Nice, I actually try to connect all of my apps to Claude because that's a default app that I always keep ON. Good to see Databox got an MCP.

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.

every marketer I know already pastes their numbers into chatgpt and asks 'what happened last week.' the fact that you're just connecting the data directly so the AI actually has real context instead of whatever we copy-paste is one of those obvious ideas that should've existed sooner

I tested Databox MCP against some of the scenarios I use most often in client work - cross-channel performance comparisons, weekly trend checks, flagging anomalies in paid acquisition. In every case, connecting through MCP and asking conversationally was faster than navigating dashboards manually. The answers referenced real metric data, not approximations. For anyone who spends time preparing performance summaries, the productivity difference is immediately obvious.

Sounds very interesting.

I actually do upload a google sheet of my company stats which includes revenue and marketing data. I have a Claude Project that analyzes the google sheet and then creates a dashboard. This solution is very interesting and more dynamic.

Hi Product Hunt! 👋

I'm Pete from the Databox team, and today we're excited to share something we've been building for a while: Databox MCP.

Every team we talk to uses AI for writing, planning, and thinking through problems. When it comes to performance data, teams are still piecing it together by hand. Someone asks "why did my ad cost spike last week?" and answering takes 20 minutes of combing through multiple dashboards, adjusting date ranges and filters.

Some teams have shortcut this by uploading a CSV to Claude. The answer sounds confident, but it’s built on context that the AI doesn’t have. No metric definitions. No historical trends. No understanding of how their business measures success. The answers are hard to trust, and even harder to act on.

Databox MCP closes that gap. 

Databox connects to all of your tools, then it feeds the AI tools with data, analysis and insights. You ask questions in plain language, and the answers come grounded in your real business data: your metric definitions, your historical context, and the way your team measures success.

Here are a few things you can do with it: 

• Get fast answers without leaving your AI tools: Ask "why did ad cost spike last week?" and your AI pulls the answer from your trusted data, and gives you a written explanation with visual context. 

• Point your AI at any of your dashboards:   Say "analyze my Google Ads dashboard" or "summarize my client reporting dashboard," and your AI knows which metrics to pull. You skip the setup work that usually goes into every AI prompt.

• Push new data into Databox from your AI: Upload a CSV or pull from an API in your AI conversation, and your AI sends it to Databox as a clean, structured dataset. Analyze it the same minute alongside the metrics you already track.

• Rely on Databox for mathematical analysis: Whether it's simple things like understanding wether an increase in a number is good or bad, or more complicated things like calculating correlations or detecting anomalies, Databox is doing the math the same every time.

• Turn recurring work into workflows: Connect MCP to n8n or Make, and your recurring AI analysis runs on its own. Schedule the Monday performance summary, trigger alerts when key metrics change, and send executive summaries that arrive with the context built in.

We soft-launched it in February, and the most interesting thing has been watching what customers do with it. Rick Kranz used the Databox MCP with Claude to turn traffic, search, and CRM data into weekly content creation recommendations. He even made the skill available for others to download. Agency operations leaders like Gary Magnone started using it to spot the root cause of KPI spikes in minutes instead of hours. High volume digital advertising agency owners like (like Kamil Rextin) used it to build paid media benchmarks from client data. Island, a software development firm used it to automate data analysis for 25 leading online publications, cutting reporting time by 96%!

It takes 60 seconds to connect and is available on all paid Databox plans. 

We'd love your input 👇

What's the one performance question your team asks every week - but still takes too long to answer?

Thanks for checking it out 🙏