Today, Google Cloud Data Agent Kit is generally available. Data Agent Kit is a free set of Model Context Protocol (MCP) tools and agent skills that lets the coding agent you already use work directly with your Google Cloud data products, whether you're using Antigravity, Claude Code, Codex, or other popular tools.

With GA, we are adding support for BigQuery Graph , Bigtable , and Managed Service for Apache Spark access to your open Lakehouse, along with dozens of quality-of-life improvements that make everyday work faster and smoother.

Coding agents have become remarkably good at writing SQL, PySpark, and pipeline code. What they don't have by default is context about your environment: which tables exist, how they're partitioned, which ones your team trusts, or why last night's job failed. Without that, even a strong agent has to work from assumptions, and you end up pasting schemas and error logs into the chat to fill in the gaps.

Data Agent Kit fills that gap with two things:

MCP tools: Connections to more than 15 Google Data Cloud services, so your agent can inspect schemas, run queries, read job logs, and manage resources in your live environment.

Google-authored skills: Open-source instructions from Google Cloud engineers that teach your agent data best practices, like optimizing BigQuery SQL, designing Bigtable row keys, and building dbt ( data build tool ) pipelines.

You can use Data Agent Kit wherever you already work: as an IDE extension for VS Code, Antigravity IDE, Cursor, and other VS Code-compatible editors; as a plugin for Antigravity 2.0, Antigravity CLI, Claude Code, and Codex; or in Cloud Shell and Cloud Workstations , where it comes pre-installed. The IDE extension also brings a lightweight version of the Google Cloud console into your editor, so you can browse data, run queries, and review your agent's work without switching windows.

Say you ask your agent, "Forecast next month's demand for our top-selling products and check whether we have enough inventory to meet it." Data Agent Kit loads the relevant skills, so the agent follows Google's best practices for the task. It searches Knowledge Catalog to find the sales and inventory tables your team trusts. It then uses MCP tools to run a forecast in BigQuery , check current stock levels in AlloyDB for PostgreSQL, and bring the combined answer back to your editor or terminal. Every step runs with your own IAM permissions.