## Use Cases and Examples

Databox MCP unlocks a variety of powerful use cases by combining Databox's data platform with AI's flexibility. Here are some examples of what you can do:

* **Conversational Data Analysis:** Ask complex questions and get immediate answers backed by your data. For example, a user could ask *"Which product category has the highest refund rate, and is it getting worse?"* in a chat interface. The AI (via MCP) translates this into a SQL query across the All_Orders dataset (e.g. grouping by product category and time period) and returns: **Highest Refund Rate: Electronics (12%)**, with an insight that *"the rate increased from 8% in Q1 to 12% in Q3, indicating a worsening trend."* This all happens without the user writing a single line of code or SQL — the AI handles the query and explanation.
* **Multi-Source Data Merging:** Easily combine data from different sources on the fly. For instance, you can upload two files (say, fb_costs.csv containing ad spend and shopify_revenue.csv containing sales) and instruct the AI: *"Merge these by Date and tell me my daily ROAS."* Using MCP, the AI ingests both files into Databox, performs a join on the Date field, calculates the **Return on Ad Spend (Revenue/Cost)** for each day, and returns the results. The insight might be *"Average ROAS was 2.4x, with a critical alert on Oct 12th (ROAS dropped to 0.8x when spend doubled but revenue stayed flat)."* This showcases how Databox MCP can do ad-hoc analysis that would otherwise require cross-tool manual work.
* **Automated Decision Triggers (Integrations with Workflows):** Because any MCP-compatible system (not just chatbots) can use the protocol, you can integrate Databox into automation workflows. For example, using the n8n automation tool, you could set up a daily job that queries Databox via MCP for a KPI and then triggers actions based on the result. Imagine an **auto-alert for marketing spend**: *Every day at 6 AM, n8n asks Databox MCP:* "Is the 3-day moving average of ROAS below 1.5?" — If yes, the workflow pauses the Google Ads campaign and posts a Slack alert to the team. This kind of multi-step logic (calculating a moving average over time, then acting) is made possible by Databox holding the historical data and performing the analysis, which simple in-app alerts couldn't handle.
* **Data Quality and Cleanup via AI:** Databox MCP can help prepare and clean data, not just analyze it. For instance, you might feed in a poorly formatted export (with messy headers or mixed data formats) and instruct, *"Clean this up and add it to my Q3 Expenses dataset."* The AI can parse and normalize the data (e.g., remove junk rows, unify currency formats), use ingest_data to append it to the existing dataset, and then perhaps run an analysis. One user did this for an expense report — after cleanup and ingestion, a query_dataset_with_ai was run to categorize spending, and the result highlighted that *"Software Subscriptions account for 40% of Q3 spend, significantly higher than in Q2."* All of this was achieved by a simple prompt instead of manual spreadsheet work.
* **Automated Reporting:** With MCP accessible to scheduling tools, you can automate routine reporting. For example, an **n8n + Slack daily report** can be set up (as highlighted in a use case card): every day at 9am, n8n triggers a query like *"Yesterday's metrics"*, formats the result into a summary, and posts it to a Slack channel. Teams can get updates without anyone logging into a dashboard. This lowers the barrier for data-driven insights to reach stakeholders in real time.


These are just a few scenarios — essentially, **any situation where you need to get data into Databox or out of Databox in an intelligent, automated way is a fit for MCP**. Whether it's interactive Q&A with your business data, or using Databox as a decision engine inside an AI agent workflow, MCP provides the bridge to do so.

*(If you need more inspiration, see the "Use Case Examples" or tutorials in our docs and community forums, which showcase story-driven scenarios ranging from sales commission calculations to automated KPI-triggered rewards.)*