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
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.
Databox
@tadej_kelc , this is exactly the workflow we had in mind - cross-channel comparisons, trend checks, anomaly detection, all conversational. The "real data, not approximations" part is what makes it useful for client work rather than just a party trick. Thanks for putting it through real scenarios and sharing the results here!
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.
Databox
@laszlo_fazakas this is one of the best use cases we've seen built on top of Databox MCP - confidence scoring based on data lineage is exactly the right architecture for high-stakes ad spend decisions. Love that you open-sourced it so other agencies can run it too. Share the GitHub link here so others can check it out!
Databox
@laszlo_fazakas what you built is the future, Laszlo.
Systems that draw insights, prioritize actions, then measure the impact of those actions.
Your system learns from it's work, like any expert in any job does.
Except that your system can draw on a way better, longer, more thorough memory and it can't quit... like a human can.
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.
Databox
@himani_sah1 Claude is exactly where we'd start too - it's the smoothest experience with Databox MCP right now. Your business data is already in Databox, now it's one connection away from being live in Claude. Hope you enjoy it!
The Incident Challenge
Great product! Is the automation itself by n8n?
Databox
@avi_ct n8n is one of the automation tools we officially support - we have templates ready at n8n.io for common workflows like weekly performance reports and Slack alerts. But Databox MCP works with any automation platform that supports MCP, so you're not locked in to n8n!
Databox
One more thing worth sharing. The most fun part of the soft launch has been watching what partners built on top of the Databox MCP, then gave away for free. A few you can grab or look at right now:
László Fazakas open-sourced Arcanian OS, a system for managing complex, multi-market client campaigns that uses the Databox MCP to run automated in-depth analysis and inform daily decisions. The whole thing is on GitHub: https://github.com/arcanianHQ/arcanian-os
Max Traylor built Mantis, an AI-powered agency account management system that automates reporting, protects retention, and surfaces upsells. Here's how it works: https://claude.ai/public/artifacts/7b5265c8-2b0c-47d9-ae69-bbdbb86ab113
Jovan Miljevic built an n8n workflow that monitors SEO cannibalization across Databox, Google Search Console, and Slack, running on its own on a schedule. It's published here: https://n8n.io/workflows/15691-ai-powered-seo-cannibalization-monitor-databox-google-search-console-and-slack/
Rick Kranz gave away three Claude skills that use the MCP to analyze different parts of a sales and marketing funnel automatically. Free to download: https://www.linkedin.com/feed/update/urn:li:activity:7455235813786861568/
Keith Gutierrez built a pipeline that flags an underperforming page, audits it, writes the fixes, and updates the CMS, with no one touching a spreadsheet. One page it touched is up 62% in sessions.
A couple of how-to walkthroughs from partners, if you'd rather see it applied to a specific job:
Gary Magnone, on finding the root cause of a KPI spike in minutes instead of hours: https://databox.com/how-to/identify-the-root-cause-of-kpi-spikes-faster-with-ai-powered-analysis
Kamil Rextin, on building paid media benchmarks from client data: https://databox.com/how-to/create-paid-media-benchmarks
Different problems, same foundation underneath. If you build something with it, I'd love to see it.
I'm loving the Databox MCP and honestly, the one thing I didn't anticipate was how useful this could be for upskilling junior team members in the marketing agencies I work with.
I sit at the intersection of ops, strategy, delivery, and client services for agency teams, and the hardest part to scale has always been the month-end report analysis. We've always needed to pair every account with a dedicated senior strategist to look at the work, the numbers, the objectives, and then tell a strong, client-facing story about what's going on and what to do about it. It takes YEARS to build that kind of instinct, and it's not practical to assume your more junior folks can step in and handle it.
With the Databox MCP, you've just fast-forwarded years of experience. An AM or coordinator can ask why a number moved, get a real answer pulled from the actual metrics, provide context around the program and goals, collaboratively hypothesize around what's happening, then show up to the client call with a proactive point of view instead of a dashboard and a promise to "have the team look into it".
Game changer. Stoked for this new evolution of the Databox platform!
ZeroHuman.
What’s the biggest productivity gain teams usually get after connecting Databox MCP? Faster reporting, better decisions, fewer manual data checks?
Databox
@byalexai from what we hear most - it's the elimination of the "data prep" step before every meeting or report. The time between "I need to know X" and "I have a reliable answer" goes from 30 minutes to 30 seconds. That shows up as faster reporting, but the real unlock is that people start asking questions they would have skipped before because the effort wasn't worth it.