Databox MCP: Chat with your data. Anywhere.
Connect Databox to Claude, n8n, Cursor, and any MCP-compatible AI tools. Ask questions in plain language, push data from any source, and get instant insights, without dashboards, SQL, or setup.
For years, getting insights from business data meant one of two things: build dashboards and check them manually, or write SQL queries and wait for answers. Both approaches assume humans are the primary interface for data.
That assumption is now wrong.
AI agents are becoming the way teams interact with data. They ask questions, they analyze trends, they make decisions. But these agents need clean data inputs and reliable ways to reason about numbers. Most data infrastructure was not built for this.
We built Databox MCP to fix that.
What Databox MCP Actually Does
Databox MCP turns Databox into a data and insights layer that any AI agent can use.
Your existing data, now accessible to AI → If you already use Databox, you have data sources connected, Google Analytics, HubSpot, Stripe, your database, and dozens of other integrations. All that data, all those pipelines you have already built, are now queryable by any AI agent through MCP. No new setup. Your existing Databox becomes an AI-ready data layer.
New data flows in just as easily → Need to push custom data? CSV exports, JSON payloads, API responses, internal metrics,… You can ingest anything through the same protocol. No connector configurations. No integration work. Just data in.
Insights come out → Ask questions in plain English. The AI taps into your existing datasets and data sources, runs the analysis, and returns answers. Not charts. Not dashboards. Answers.
This creates a closed loop: connect your tools (or use existing connections), push additional data if needed, ask questions, and get insights. All through the same interface. All are accessible to any tool that speaks the Model Context Protocol.
Why Databox MCP Is Different
Here is a dirty secret about most AI data tools: the LLM is doing the calculations. It reads your numbers, tries to compute averages, and hallucinates the results. Sometimes it gets close. Often it does not. You cannot build serious business decisions on approximate math.
Databox MCP works differently. The LLM never touches your calculations.
All analytics run on the Databox Agentic Platform → a real data infrastructure stack built for accuracy:
Data Platform → Your data lives in structured datasets with proper schemas, types, and validation. Data pipelines handle ingestion, transformation, and storage. This is not a CSV sitting in an LLM’s context window. This is a real data layer.
Analytic Query Engine → When you ask a question, the system generates and executes actual queries against your data. Aggregations, joins, filters, time-series calculations. All performed by a query engine designed for analytics. Not by a language model guessing at arithmetic.
Semantic Layer → The platform understands what your data means, not just what it contains: business definitions, metric relationships, dimensional hierarchies. When you ask about “revenue,” the system knows exactly which columns, which filters, and which calculations apply.
Genie, our AI analyst, orchestrates this entire stack. It interprets your question, determines the necessary analysis, selects the appropriate tools, executes queries against the platform, and gathers insights from real computations.
The LLM’s job? Summarize the results in plain language. That is it.
This architecture matters. When you ask, “What was our ROAS last week?” you get the actual number from an actual calculation, not a probabilistic guess from a model that cannot reliably multiply.
Accuracy is not optional for business data. We built the platform to guarantee it.
Why This Matters Now
The shift to AI-native workflows is happening faster than most companies realize. Teams are already using ChatGPT, Claude, and custom agents to draft documents, analyze code, and summarize meetings. The next step is obvious: these agents need access to business data.
But there is a gap. Most data platforms were designed for humans clicking through UIs. They require manual setup, predefined dashboards, and point-to-point integrations. None of that works when the user is an AI agent running at 3 AM.
Databox MCP closes this gap. It exposes data and insights through a protocol that agents understand natively. No dashboards required. No human in the loop. Just data in, answers out.
What You Can Do With It
Replace manual data cleanup.
Upload a messy export, garbage headers, mixed formats, inconsistent dates. The AI cleans it, normalizes it, and adds it to your dataset. What used to take an hour in Excel now takes seconds.
Ask questions instead of building reports
“Which product category has the highest refund rate?”, “Is our ROAS trending up or down this week?” “Compare Q1 vs Q2 performance.” You get answers, not dashboards to interpret.
Merge data sources without engineering
Combine ad spend from one file with revenue from another. Calculate ROAS by day. Spot anomalies. The AI handles the join logic and the math.
Automate decisions on real analysis
Set up a workflow that asks: “Is the 3-day moving average of our conversion rate below target?” If yes, trigger an alert. If no, do nothing. The decision is based on calculated trends, not static thresholds.
Give everyone access to insights
Your founder can ask high-level questions. Your analyst can run deep dives. Your automation tools can query at scale. Same interface, same data, different depths.
The Difference: Open, Secure, Agentic
Most data platforms are closed systems. Data goes in through specific connectors. Insights come out through specific dashboards. Everything requires configuration.
Databox MCP is different:
Open ingestion → Any data, any format. Your data can come from databases, APIs, spreadsheets, custom tools, financial systems, whatever you use. One protocol handles it all.
Secure by design → OAuth 2.0 authentication. JWT token validation. Scope-based authorization. Not an afterthought, the foundation.
Agentic interface → AI is not an add-on feature. It is the primary way to interact with data. The platform was designed for agents from the ground up.
Universal protocol → MCP is a standard. Any tool that implements it can connect. No vendor lock-in. No proprietary APIs. Just a clean, documented interface.
No setup required → Push data, ask questions. The AI figures out the schema. The AI writes the queries. The AI interprets the results. You just ask.
Where This Is Going
Databox MCP is the foundation for what we are building next.
Imagine agents that proactively surface insights. You wake up to a message: “Revenue dropped 15% yesterday, primarily driven by a 40% decline in the Electronics category. Here are three possible causes based on your data.”
Imagine automated workflows that run end-to-end. Data flows in from your tools overnight. Analysis runs automatically. By morning, you have recommendations waiting, not just numbers, but suggested actions.
Imagine a single data layer that sits beneath every AI agent in your stack. Your sales agent knows revenue numbers. Your support agent knows customer history. Your marketing agent knows campaign performance. All pulling from the same source of truth. All properly authenticated. All auditable.
This is where we are headed. Databox is the intelligence layer for business data. Not a place to check metrics → a system that understands your business and helps agents reason about it.
The Bottom Line
Data platforms were built for humans. Dashboards. Reports. Manual queries.
AI agents need something different. They need data that is easy to ingest, easy to query, and easy to reason about. They need a protocol, not a UI. And they need security that does not compromise.
Databox MCP is that foundation. Open protocol. Enterprise auth. AI-native design.
Push any data. Ask any question. Get real answers. Let your agents and your team → focus on decisions instead of data wrangling.
Get Started
Authenticate through OAuth. Connect your MCP client. From then on: push data, ask questions, get answers.
The future of business data is not dashboards. It is AI that understands your numbers and tells you what matters → with security you can trust.
We built the foundation. Now it is your turn to build on it.
Visit https://databox.com/mcp for more information and setup guidelines.