<p>We built Databox MCP because of a pattern we kept seeing: teams were doing their thinking in Claude and ChatGPT, but their actual performance data lived elsewhere. So they'd export it, paste it in, and hope the AI understood it. It didn't. The data was already in Databox, connected, defined, with all the historical context. It just wasn't reachable from the tools where people were actually working. MCP closes that gap. One connection, and your AI can talk about your real numbers instead of guessing.<br></p><p>This is the part that matters more than people realize. An AI is only as good as the data layer underneath it. Databox isn't a pile of raw exports; it's a governed semantic layer: metrics defined once and consistently, data cleaned and modeled across all your sources, with the historical context that tells you whether a number is actually good or bad. That's the difference between an answer you can act on and a confident guess you have to double-check.<br></p><p>Asking questions and getting trusted answers is the obvious first use. What I'm most excited about is what comes next: workflows that act on the data on their own. Performance management, monitoring, and decisions that trigger automatically. Your AI stops being something you ask and starts being something that keeps the business moving week to week.<br></p><p>Proud of the team for shipping it.</p>
<p>Hi Product Hunt! 👋<br></p><p>I'm Pete from the Databox team, and today we're excited to share something we've been building for a while: <strong>Databox MCP.</strong><br></p><p>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.<br></p><p>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.<br></p><p>Databox MCP closes that gap. <br></p><p>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.<br></p><p>Here are a few things you can do with it: </p><ul><li><p><strong>Get fast answers without leaving your AI tools:</strong> 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. </p></li><li><p><strong>Point your AI at any of your dashboards: </strong> 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.</p></li><li><p><strong>Push new data into Databox from your AI: </strong>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.</p></li><li><p><strong>Rely on Databox for mathematical analysis: </strong>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. </p></li><li><p><strong>Turn recurring work into workflows: </strong>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.</p></li></ul><p>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 <a href="https://ai-marketinglabs.com/claude-cowork-skill-growth-dashboard%20 " target="_blank" rel="nofollow noopener noreferrer">skill available for others to download</a>. Agency operations leaders like Gary Magnone started using it to <a href="https://databox.com/how-to/identify-the-root-cause-of-kpi-spikes-faster-with-ai-powered-analysis)" target="_blank" rel="nofollow noopener noreferrer">spot the root cause of KPI spikes</a> in minutes instead of hours. High volume digital advertising agency owners like (like Kamil Rextin) used it to <a href="https://databox.com/how-to/create-paid-media-benchmarks" target="_blank" rel="nofollow noopener noreferrer">build paid media benchmarks from client data</a>. Island, a software development firm used it <a href="https://www.linkedin.com/posts/marketingagency-clientreporting-aiagents-share-7459669857157132289-XHhO/ " target="_blank" rel="nofollow noopener noreferrer">to automate data analysis for 25 leading online publications</a>, cutting reporting time by 96%! <br></p><p>It takes 60 seconds to connect and is available on all paid Databox plans. <br></p><p>We'd love your input 👇
<p>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.</p>
