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Data Agent Kit is now GA: Bring Google Data Cloud to any coding agent Arun Nair Product Manager, Google Jeff Nelson Developer Advocate, Google 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. What is Data Agent Kit? 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. How it works 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. How Data Agent Kit connects to data. What you can build Data Agent Kit covers analytics, operational databases, the Lakehouse, and pipelines. Here's what that looks like in practice, starting with what's new at GA. Analytics and graph: BigQuery and BigQuery Graph (New in GA) Graphs are a natural way to explore relationships, like which products people buy together or how suppliers connect to your inventory. Building one usually means hand-writing CREATE PROPERTY GRAPH DDL, learning GQL, and working out which keys actually form edges. Instead, you describe the graph you want and your agent builds it. The bigquery-graph-author skill maps your tables to nodes and edges, checks each proposed relationship against the actual data, and shows you a plan to approve before creating anything. It can even start from an ER diagram or data model you already have. The bigquery-graph-query skill then writes the GQL, and the graph visualizer in the IDE lets you click through the results. Building and visualizing a BigQuery property graph. Operational and real-time databases: Spanner, AlloyDB, Cloud SQL, and Bigtable (New in GA) Some features have to load instantly, like a personalized feed, a live counter, or a "recently viewed" rail on your storefront. Bigtable is built for exactly that, and it rewards a well-designed row key. With GA, Bigtable joins Spanner, AlloyDB, and Cloud SQL as a fully supported database in Data Agent Kit. The new bigtable-basics skill designs your schema around how the data will be read and flags hotspots and full table scans before you create anything. Your agent can then create the table and query it with GoogleSQL, with column families flattened into readable columns. In the IDE, you can browse Bigtable instances and tables in the catalog explorer and run queries from the SQL editor. Agent-guided Bigtable schema design and querying. Lakehouse and Spark: Managed Service for Apache Spark and Lakehouse for Apache Iceberg (New in GA) Your agent could already query the Apache Iceberg tables in your Lakehouse through BigQuery. Now it can also work with those same tables using serverless Spark on Managed Service for Apache Spark, with no cluster to manage. Each session keeps its state, so temporary views carry across statements, and you get Iceberg's full feature set, including branching, time travel, and schema evolution. Your tables don't all have to live on Google Cloud, either. The federate-lakehouse-catalog skill connects your Lakehouse to AWS Glue and Databricks Unity Catalog, so your agent can query that data in place without building an ingestion pipeline first. Branching and querying Iceberg tables with Managed Service for Apache Spark. Pipelines and orchestration: dbt, Dataform, and Managed Service for Apache Airflow Once your logic works, your agent can turn it into a pipeline that runs on its own. It writes dbt or Dataform models, then the gcp-pipeline-orchestration skill schedules them together with your notebooks as an Orchestration Pipeline on Managed Service for Apache Airflow. Pipelines can also include Gemini Enterprise Agent Platform steps, like uploading a model or running batch inference. In the IDE, you can follow each run on a visual pipeline canvas. If a task needs attention, click Diagnose to hand its logs to your agent. Troubleshooting skills for Airflow and Spark trace the root cause and propose a fix for you to approve. Troubleshooting a failed Airflow DAG with an agent. Improving the developer experience GA also streamlines setup and day-to-day workflows. When you get started, you simply sign in once and select the Google Cloud services you use. Data Agent Kit automatically enables the required APIs, installs the matching skills, and configures your MCP servers with no manual setup files. Inside the IDE, the SQL editor and notebooks now support inline code generation, @ references to tables, and diff views for suggested changes. The extension also shares your active project, open file, and the error from the query you just ran with your agent, so asking it to "fix this query" just works. We also made the core tools faster and more responsive. New Spark notebooks automatically create and select a Spark Connect runtime, and Spark SQL queries in the editor run in isolated sessions with built-in execution metrics. The catalog explorer now loads faster and includes BigQuery public datasets in the sidebar. Tuned notebook skills help your agent finish notebook tasks more quickly while using fewer tokens. Ready for the enterprise Data Agent Kit is included at no additional cost; you pay standard pricing only for the Google Cloud services your agent uses. Skills also steer the agent toward cost-aware query patterns, like checking partition keys and running a dry run before executing a BigQuery query to avoid accidental full-table scans. And because the skills are open source on GitHub, your team can audit them, fork them, or write custom skills for your own internal standards. For access control, the agent connects as you or as a service account you impersonate, so row- and column-level security policies apply automatically. Admins can also govern MCP access with Identity and Access Management (IAM), screen MCP traffic with Model Armor, and scope agents with VPC Service Controls and Principal Access Boundary policies. Install and get started You can set up Data Agent Kit in under a minute in either your IDE or your terminal. Option 1: Install the IDE Extension - IDE extension: Search for "Google Cloud Data Agent Kit" in the Extensions panel of VS Code, Antigravity IDE, Cursor, or any VS Code-compatible editor. You can also install it from the VS Code Marketplace or Open VSX. - Antigravity 2.0: Go to Settings > Customizations > Build with Google Plugins, then download Data Agent Kit. - Cloud Shell and Cloud Workstations: Already installed by default; just open the editor and sign in. Option 2: Install the CLI Plugin Run the command for your preferred coding agent using the official GoogleCloudPlatform/data-agent-kit-plugin repository: Then try a first prompt: Data Agent Kit plugin running in Claude Code. Next steps & hands-on resources - Read the docs: Explore the Data Agent Kit documentation and product overview page. - Explore the skills: Browse, star, and contribute on GitHub. - Build an analytics workflow: Try the Analytics with Data Agent Kit and Antigravity IDE codelab, and read the companion blog, Agentic analytics with the Data Agent Kit. - Build a data science pipeline: Try the Fraud detection pipeline with Data Agent Kit and Antigravity IDE codelab.
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