Imagine your director sends you a chat message Monday morning: Our average order value dropped 7% in January, but total revenue stayed flat. If you’re a data practitioner, you know why these types of questions can be tough. They’re totally open ended. There’s not a single root cause dashboard you can open. Was there an error in the web logs? Was a promo code misconfigured? You won’t know until you start digging, and you rarely find the answer in just one place.
Each piece of the answer lives somewhere different in your environment: Sales history (orders and line items) sits in a data warehouse Live customer records are in a production PostgreSQL instance Marketing campaign rules are raw JSON files in an object store Writing any one of these queries is easy. You’ll write the same one a dozen times, tweaking WHERE clauses or adding subqueries to find the answer. Then you’ll bounce to the next system and start again with a different dialect. Before you know it, you have ten browser tabs open and a whole afternoon gone, all to answer one question.
The Data Agent Kit is built to solve this issue. It is a set of MCP servers and agent skills that helps data developers run data workflows from their IDEs. It’s available both as an extension for VS Code forks (Antigravity IDE, Cursor) and as a plugin for other tools (Antigravity 2. 0, Antigravity CLI, Claude Code, Codex), so you don’t need to leave your IDE to get answers. Model Context Protocol (MCP): an open standard that connects your agent to tools, databases, and remote cloud infrastructure. Skills: markdown files that augment your agent’s knowledge, teaching it how to interact with your specific stack.
Instead of generating SQL snippets and copy-pasting them into a console, Data Agent Kit lets agents run the queries and read the results on your behalf. Let’s see what this looks like in practice applied to the average order value scenario. In this setup, the data warehouse is BigQuery , the Postgres instance is Cloud SQL , and the campaign rules sit in Cloud Storage . The investigation begins in the IDE’s chat pane with the following natural language prompt to confirm the baseline numbers: The agent processes your prompt, invokes relevant skills, and prepares to start querying your data.