How an AI Assistant Can Work With Your Business Data with MCPs

Imagine asking your AI assistant:

“What were our top-selling products last quarter, and which sales rep closed the most deals?”

And instead of getting a generic answer or being told to check your dashboard, the AI pulls the exact numbers from your company’s data and gives you a real answer in seconds.

This is no longer science fiction. A new technology called MCP (Model Context Protocol) makes this possible. It’s a standardized way for AI tools to securely connect to your business intelligence and analytics platforms and actually work with your real data.

The big players—Tableau, Power BI, Looker, and Databox—have all built MCP connections. But here’s what most people don’t realize: they don’t all work the same way. Some let your AI read your data. One lets your AI write data too. That difference matters more than you might think.

What Exactly is MCP, and Why Should You Care?

Think of MCP like a universal translator between AI assistants and business software.

Before MCP, if you wanted an AI to work with your Tableau dashboards, someone had to build a custom integration. Same for Power BI. Same for every other tool. It was expensive, fragile, and most companies couldn’t do it.

MCP changes that. It’s an open standard that lets any AI assistant (Claude, ChatGPT, Copilot, and others) connect to any business tool that supports it—using the same “language.” Anthropic released it in late 2024, and the major BI and analytics vendors have been racing to adopt it ever since.

The practical benefit: Your team can ask questions about your actual business data in plain English, and get answers that reflect what’s really happening in your company—not just what the AI was trained on.

The Big Picture: Readers vs. Writers

Here’s the most important thing to understand about business intelligence and analytics BI platforms and MCP:

Most platforms only let AI read your data. Tableau, Power BI, and Looker all fall into this category. Your AI can ask questions and get answers, but it can’t add new information or make changes.

One platform—Databox—also lets AI write data. This might sound like a small distinction, but it opens up entirely different possibilities.

With a read-only connection, your AI can tell you “Sales dropped 15% last week.”

With a read-write connection, your AI can detect that drop, automatically adjust your ad spend through another integration, and record that it made the adjustment so you have a full audit trail.

How Each Platform Stacks Up

Tableau: Simple and Familiar

If your company already uses Tableau, their MCP connection is the easiest starting point. It gives AI access to your existing dashboards and data sources with minimal setup. The downside? It’s read-only, and the feature set is basic—13 tools, focused mainly on querying what you’ve already built. Good for organizations that want to dip their toes in without disrupting anything.

Power BI: Enterprise-Grade, Enterprise-Complex

Microsoft took a more cautious approach. They actually built two separate MCP connections: one for developers to build and modify data models locally, and another for regular users to query data in the cloud. This prevents accidents—an AI can’t accidentally break a dashboard that 10,000 people rely on. But it also means more complexity to set up and manage. Still read-only for end users, but with strong governance controls.

Looker: The Most Powerful Reader

Google’s Looker has the most comprehensive MCP implementation of the traditional BI tools, with 32 different capabilities. Its strength is the “semantic layer”—instead of querying raw database tables, your AI asks questions using business concepts your team has already defined (like “gross margin” or “qualified lead”). This leads to more accurate answers. The trade-off: it’s slower, more expensive to run, and any new data source requires a developer to model it first, which can take days.

Databox: Built for AI from Day One

Databox is the outlier. While the other platforms retrofitted AI connections onto existing software, Databox was designed with AI agents in mind. Its killer feature is the ability to ingest data—meaning AI can write information back into the system, not just read it. This enables closed-loop automation: detect a problem, take action, record what happened. The platform is simpler (10 tools vs. Looker’s 32), but those tools are focused on real-time operations rather than historical reporting.

Quick Comparison

Which One is Right for You?

This comes down to what you need AI to do.

If governance and consistency are your priorities—you’re in a regulated industry, you need audit trails, and you want AI to use only officially-approved definitions of your business metrics—stick with Tableau, Power BI, or Looker. They’re read-only by design, which means less risk of something going wrong.

If speed and automation are your priorities—you want AI to not just identify problems but actually help fix them, and you need to act on data in real-time—Databox is currently the only platform built for that use case.

If you’re already invested in one platform, start there. The easiest MCP connection is the one that works with tools your team already knows.

The Bottom Line

MCP is making it dramatically easier to connect AI assistants to your real business data. All four major platforms now support it, which means the question isn’t whether your AI can access your BI and analytics tools—it’s how you want it to interact with them.

The traditional players have built secure windows into your historical data. That’s valuable. But if you’re looking ahead to a world where AI doesn’t just answer questions but actually helps run parts of your business, you’ll want a platform that lets it write, not just read.

The technology is ready. The question is: what do you want your AI to do?