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MCP Connectors by Databox

Give your AI Analyst context to explain performance and act

Analytics
SaaS
Artificial Intelligence
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Hunted byRohan ChaubeyRohan Chaubey

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.

Top comment

Hi Product Hunt! 👋

I'm Pete from Databox. Today we're launching MCP connectors for Genie, our AI Analyst.

Genie analyzes your live metrics and tells you what changed. But a metric alone rarely explains what's going on.

Trial signups dip, and the reason is a pricing change your team shipped two weeks ago. Traffic jumps, and behind it is an influencer mentioning you on reddit that none of your analytics can track. The numbers alone can't tell you why something happened.

The deal context in your CRM, the tickets in your support desk, the tasks in your project tool and the Slack messages where your team discussed that unexpected Reddit mention. That's the explanation, and it's spread across the tools where work and conversations happen.

That context is what separates a generic report from a useful one. Any AI can look at a chart and tell you a number fell 12%. Knowing that two big deals slipped because you didn't anticipate a more extensive legal review, support tickets spiked after a feature release, and the reasons your ad strategy changed mid-month is what makes a report useful and actionable. Until now, getting that kind of answer meant someone gathering the context by hand, every time.

MCP connectors give Genie that context. Connect your tools, and Genie pulls from them during analysis, so its answers reflect what's actually happening in your business. 

In addition to automating reporting, you can also use Databox's new MCP connectors to execute actions in other tools, based on your analysis. Want to shut down an ad campaign once it stops performing? No problem. Just write a skill in Databox that instructs your ad platform to shut down a campaign once frequency hits 5 and conversion rate drops below 1%. Want to update content on a website page after it stops getting search traffic? Easy. Want to create a task for your sales team if they have too many deals open without a next step? Even easier.

How it works:

  • One-click connectors: HubSpot, Slack, Notion, Linear, Mixpanel, Semrush, Klaviyo, Ahrefs, and more, with 10+ available at launch

  • Custom connectors: add any MCP server by URL, with OAuth, API key, or bearer token support

  • Permissions you control: set every tool to always allow, needs approval, or blocked, so Genie only acts where you've said it can

  • Skills & Routines: Write skills that pull data from specific integrations and context from specific MCP servers, automate actions based on the analysis. Run it completely autonomously using Routines.

MCP connectors are live today: https://databox.com/

If there's a tool you want Genie connected to, tell us below.

Thanks for checking it out 🙏

Comment highlights

The weekly report that used to take half a Thursday now has a roommate doing it. I'm in!

#1 well deserved 🥇 giving the analyst context from crm + support is the usefull part. which connector gets used most?

The per-tool approval settings are the part I’d want in any setup like this. Pulling context is one thing, but once an analyst can update a CRM or stop a campaign, “needs approval” can’t be an afterthought.

"it can act on what it finds" is the line I'd want to poke at. explaining why a metric moved is read-only and low risk, but acting implies it's writing something back into the CRM or support desk, not just reading from it. what does that write path actually look like in practice, is it drafting a task for a human to approve, or can Genie create/update records in the connected tool directly without a person in the loop first?

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.

Bringing MCP connectors makes the “why behind the numbers” much more useful.

Jakob, on the approval default: how does that work for a Routine running on a schedule, when nobody's in the chat to say OK? I run a few agents unattended on a Mac Mini and that's the case I'd worry about. A routine waiting on approval and a routine that found nothing to do look much the same from the outside, unless one of them says so.

Congratulations on the launch!

When the tools point to different reasons (say the CRM notes blame a legal review but a Slack thread blames pricing), does Genie show both explanations and let you judge, or does it pick the most likely one?

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

We spent a good chunk of time on connector management, so seeing people use the permission settings is a nice payoff. My setup for Notion: read tools on Always allow, everything else on Needs approval. Fast for lookups, still asks before changing anything.

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!!

How did you prioritize which connectors to ship first, and what’s your bar for a connector being ‘good enough’ (tool granularity, auditability, token/tool-manifest size, reliability of OAuth/auth flows) versus leaving it to “bring your own MCP server”?

Looking real nice! So as I understand these are a set of MCP's that I can integrate in my workflow?

The Routines feature caught my attention because it sounds useful for spotting important changes without having to constantly check dashboards.

the number one question on demo calls is basically 'ok but does it actually know whats happening in our other tools'. now i can just say yes, connect hubspot or slack once and genie explains a number using whats actually in there. way easier sell than pointing at a roadmap slide

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.

sometimes i wonder if i'm asking the right questions to my AI, this connector might help since my CRM’s already got all the context it needs. i’ll probably end up asking it about last month's sudden dip in signups and then get sidetracked with something else.

About MCP Connectors by Databox on Product Hunt

“Give your AI Analyst context to explain performance and act”

MCP Connectors by Databox launched on Product Hunt on September 28th, 2026 and earned 457 upvotes and 84 comments, earning #1 Product of the Day. 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.

MCP Connectors by Databox was featured in Analytics (173.9k followers), SaaS (44.5k followers) and Artificial Intelligence (479.9k followers) on Product Hunt. Together, these topics include over 199.9k products, making this a competitive space to launch in.

Who hunted MCP Connectors by Databox?

MCP Connectors by Databox was hunted by Rohan Chaubey. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.

Reviews

MCP Connectors by Databox has received 8 reviews on Product Hunt with an average rating of 4.75/5. Read all reviews on Product Hunt.

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