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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Congratulations to the team, this one is well deserved! As one of the engineers that have been working on the MCP connectors I am most proud of the permission setup for every tool. This is a split point between trusting the ai with your crm and never actualy adopting ai at all and skipping the opportunities it brings.
@rene_kolednik Thanks, Rene, and thanks to you and the team for building it! Fully agree. Most teams won't give AI access to their CRM if it's all or nothing. Being able to choose exactly what the AI Analyst can see and do, tool by tool, is what makes it easy to say yes.
Looking real nice! So as I understand these are a set of MCP's that I can integrate in my workflow?
@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.
@zigapotocnik Thanks for the clarification! Very useful, congratulations!🥂
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!
@aashisengar Thanks so much, Aashi! Appreciate the support today.
It's worth pointing out that compared to a normal integration, this isnt just data coming in, its tools genie can actually call. It's basically the foundation for agents later, not a standalone feature on its own.
@marcel_mumel Exactly, Marcel. An integration brings data in. A connector gives Genie, our AI Analyst, tools it can use: it can look things up, and it can act, like updating a deal or creating a task. That's what makes it the base for agents. Every connector a team adds today is one more thing their agents will be able to do later.
lfg!! congrats on the launch
@mcarmonas Thanks, Martí! Appreciate you stopping by.
Do you have mechanisms for provenance checking facts/numbers via some kind of multi-agent checks or "citing"? How do you make sure that there are no hallucinated results?
(i assumed that this might be the problem you have, but depending on structure of your soft ig you might even avoid this entirely with retrieval-based approach over generated (AI just needed to find and point to smth in the data instead of "read and then rewrite in the return response")
Congrats on the launch!!
@alp_adur Thanks, Latif, and great question. You're right that the retrieval side does most of the work here.
The numbers aren't generated. When Genie, our AI Analyst, answers a metric question, it queries your actual Databox data sources connected or uses correct MCP tool calls and works from what comes back. It doesn't recall or estimate numbers. Connectors work the same way. The context behind a number comes from live tool calls to your connected tools, so the AI Analyst is pointing at a real deal, ticket, or thread. It isn't writing its own version of what probably happened.
It also points you to the source it used, so you can open it and check. We don't claim hallucinations are impossible. No one building on LLMs honestly can. But grounding every answer in retrieved data and showing where it came from is how we keep it tight, and how you can catch it when something's off.
Hey Peter, this is a great product. Congrats on the launch!
@nathan_tarbert Thanks, Nathan, appreciate your support!
Nice, congratz! I work in support and from a technical perspective it's awesome that every connector ships with read AND write tools. Write is just turned off until permissions and oversight catch up. This is a cleaner path to v2 than adding support for write access after the fact
@emil_korpar Thanks, Emil! Every connector ships with both read and write tools, and both are live. What keeps it safe is the permission setup. You choose what Genie, our AI Analyst, can do, tool by tool, so a team can allow updating a deal but block sending anything to a customer.
Good job, team! Since launch, I managed to setup full end-2-end workflows, leveraging data retrieval via @Langchain MCP, semantic analysis of the conversations and digesting the results in the datasets. Meanwhile, I put agents to work on daily/weekly routines for insights and anomaly tracking. Unlimited data-analysis-related use cases.