MCP Connectors by Databox - Give your AI Analyst context to explain performance and act
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Connect your AI Analyst to the tools your business runs on. It pulls context from your CRM or support desk, so every answer reflects what's happening in your business, and it can act on what it finds. Choose from 10+ connectors or add any MCP server.
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Databox
I want to point out a small but super useful detail: permission settings are super detailed for each tool (toggle for allow/needs approval/blocked). This sounds like a small thing, but it's super useful cause nothing is locked in place and you can change the settings as you go depending on which data you want to bring in there.
How much setup is needed before the AI Analyst understands the company context?
Databox
@linpeng Very little, Lin. Pick a tool, click connect, and sign in. That's the whole setup, and it takes a few minutes. There's nothing to map or configure.
From there, the AI Analyst pulls in context from that tool whenever it's relevant to your question. You can adjust what it can access tool by tool, but you don't have to before you start. Most teams connect one tool first, ask a question they'd normally dig for, and add more from there.
Can it learn which business questions matter most to a specific team?
Databox
@jasonwu Good question, Jason. Today your team shapes that directly rather than waiting for it to learn on its own. With Skills, you tell Genie, our AI Analyst, how your team works: which questions matter, which metrics to look at, and how to answer. Routines then run those questions on a schedule. Connectors add each team's own tools, so sales gets answers from sales context and marketing from marketing context.
It's more predictable this way, since the team decides what matters instead of the AI guessing.
Can teams create different report formats for executives managers and individual departments?
Databox
@noahanderson Yes, Noah. With Skills, each team defines how a report should look and what it should focus on. An executive version might be a short summary with top risks and wins, while a department version goes deeper into its own metrics. Routines then send each version to the right people on schedule. Connectors add the context behind the numbers to all of them.
The business context layer seems important because numbers without context can easily lead to the wrong conclusion. How is that context added and maintained over time?
Databox
@darly_selby Agreed, Darly, that's exactly the risk we wanted to fix. The good news is that nobody has to add or maintain the context by hand. It already lives in the tools your team uses every day, like your CRM, project tools, and team chat. Once a tool is connected, the AI Analyst reads from it live whenever it's relevant. As your team updates deals, tickets, and threads, the context stays current by itself. There's nothing to copy, sync, or keep up to date.
Toone
Looking real nice! So as I understand these are a set of MCP's that I can integrate in my workflow?
Databox
@matheus_paranhos1 Thanks, Matheus! Close. It works the other way around: MCP Connectors bring your tools into Databox. You connect the tools your team already uses, either from our pre-built list or any MCP server you run yourself. Genie, our AI Analyst, can then use them to explain what's behind your numbers.
If you want to go the other direction and use your Databox data inside your own AI tools, that's what the Databox MCP is for.
Toone
@zigapotocnik Thanks for the clarification! Very useful, congratulations!🥂
Databox
Its strange how the setup itself is boring, but in a good way. Just pick a connector, click connect, authenticate and you are done...no config screen, no deep dives into the docs and spending time googling on how to make it work. Even for a custom MCP server there is just one extra step, paste the URL and the rest of the form shows up once it validates, if you have connected it anywhere else, it will be just as simple to get it working in Genie.
Big congrats to the team for building this! Unlocks insane amount of possibilities with Genie now 🚀
Congratulations on the launch!
Databox
@aashisengar Thanks so much, Aashi! Appreciate the support today.
What happens when the available data is incomplete or does not clearly explain a performance change?
Databox
@manonbriffaut Great question, Manon. When the data doesn't explain the change, the AI Analyst should say so instead of guessing. It tells you what it found, what's missing, and where the gap is. That's also where connectors help most. Often the answer isn't in the metrics at all, it's in a deal update or a team conversation. The more of those tools you connect, the fewer dead ends you hit.
Can it understand custom metrics that a company has created rather than only standard analytics metrics?