
We didn’t set out to build an MCP server.
In fact, AI at Omni started as a handful of experiments with a lot of skepticism. Data analytics requires consensus, precision, and governance, and we felt this was at odds with AI, which can be…unpredictable.
While working on an initial text-to-SQL bot, we realized we could take a different approach to AI. The semantic model our customers had already built in Omni naturally helped AI return more accurate results where other tools couldn’t. What our customers build for great BI could also be used for great AI.
Since then, we’ve made big strides to help people analyze data with confidence using AI. Our latest step is the MCP server: a new way to use Omni’s semantic model and query engine inside the AI tools you’re already using, like Claude or ChatGPT.
For a quick overview, check out this video from my teammate Conner:
In this post, I’ll walk through what the MCP server is and how to use it. I’ll also share the path we took to get here — including the experiments, learnings, and customer conversations that shaped the journey.
Introducing Omni’s MCP server #
Omni’s MCP server lets you plug into Omni wherever you’re already using LLMs (as long as they support MCPs). Anyone can ask a question about their data in their favorite AI interface, and the LLM will “borrow” Omni’s capabilities to return a result governed by your business logic and security protocols.
Data teams can set up the MCP server behind the scenes, and everyone can simply ask questions and get answers.
Unlike traditional text-to-SQL approaches, Omni’s semantic model helps prevent hallucinations and protect against data leakage. With the MCP server, AI queries inherit both access controls and business context defined in your semantic model. That means users only see data they’re allowed to access, and AI responses stay grounded in logic your team has already built.
By bringing governed data closer to the tools and workflows people already use, you unlock new use cases without steep learning curves:
Marketing #
Looking to unlock a new revenue stream, a growth marketer wants to experiment with a closed-lost re-engagement campaign. She opens Claude and asks, “Who are our top 10 closed-lost accounts by ARR?” — which returns data from Omni via MCP. With that list in hand, she follows up: “Create a HubSpot email template for our closed-lost accounts.” Because Claude has access to their marketing email library and brand messaging framework, it can generate a ready-to-send draft she can copy directly into HubSpot, helping her move from idea to campaign in just a few minutes.
Sales Enablement #
A product marketer is updating the company’s core sales enablement doc to reflect recent wins. He first connects Claude to Google Docs so it has access to the existing content. Then, he asks Claude to pull a report of recent won deals using Omni via MCP. Once the results are in, he follows up: “Based on this data and the doc, what should we update?” Claude suggests positioning updates and fresh proof points, which he adds straight into the doc. Claude’s access to Omni helps him make sense of the data and add new insights without needing to bounce between tools.
Revenue Operations #
A month before the quarter closes, a CEO asks the RevOps team for a quick sales forecast for next quarter. Instead of running the whole forecast manually, the RevOps analyst opens Claude and says: “Create a forecast for Q3 using Omni data.” Claude calls Omni via MCP to pull key inputs like closed deals, conversion rates, and average deal cycle. Then it generates a forecast and rolls it up into a concise summary for the CEO, saving the analyst hours and keeping the team focused on company goals.