# Data Agent Kit is now GA: Bring Google Data Cloud to any coding agent > Source: > Published: 2026-09-30 13:00:00+00:00 Today, [Google Cloud Data Agent Kit](https://cloud.google.com/products/data-agent-kit?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog) 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](https://cloud.google.com/bigquery/docs/graph-overview?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog), [Bigtable](https://cloud.google.com/bigtable?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog), and [Managed Service for Apache Spark](https://cloud.google.com/dataproc-serverless/docs/overview?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog) 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](https://github.com/GoogleCloudPlatform/data-agent-kit-plugin) 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](https://www.getdbt.com/)) 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](https://cloud.google.com/shell?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog) and [Cloud Workstations](https://cloud.google.com/workstations?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog), 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](https://cloud.google.com/dataplex/docs/introduction?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog) to find the sales and inventory tables your team trusts. It then uses MCP tools to run a forecast in [BigQuery](https://cloud.google.com/bigquery?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog), 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. 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. 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. 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](https://cloud.google.com/spanner?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog), [AlloyDB](https://cloud.google.com/alloydb?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog), and [Cloud SQL](https://cloud.google.com/sql?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog) 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. 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](https://cloud.google.com/dataproc-serverless/docs/overview?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog), 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. Once your logic works, your agent can turn it into a pipeline that runs on its own. It writes dbt or [Dataform](https://cloud.google.com/dataform?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog) models, then the `gcp-pipeline-orchestration` skill schedules them together with your notebooks as an Orchestration Pipeline on [Managed Service for Apache Airflow](https://cloud.google.com/composer?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog). 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. 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. 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](https://cloud.google.com/security-command-center/docs/model-armor-overview?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog), and scope agents with [VPC Service Controls](https://cloud.google.com/vpc-service-controls?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog) and Principal Access Boundary policies. You can set up Data Agent Kit in under a minute in either your IDE or your terminal. **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](https://marketplace.visualstudio.com/items?itemName=googlecloudtools.datacloud) or [Open VSX](https://open-vsx.org/extension/googlecloudtools/datacloud). **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. Run the command for your preferred coding agent using the official [`GoogleCloudPlatform/data-agent-kit-plugin`](https://github.com/GoogleCloudPlatform/data-agent-kit-plugin) repository: Then try a first prompt: **Read the docs:** Explore the [Data Agent Kit documentation](https://docs.cloud.google.com/data-agent-kit?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog) and [product overview page](https://cloud.google.com/products/data-agent-kit?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog). **Explore the skills:** Browse, star, and contribute on [GitHub](https://github.com/GoogleCloudPlatform/data-agent-kit-plugin). **Build an analytics workflow:** Try the [Analytics with Data Agent Kit and Antigravity IDE](https://codelabs.developers.google.com/dak-analytics-eng-antigravity-ide?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog) codelab, and read the companion blog, [Agentic analytics with the Data Agent Kit](https://cloud.google.com/blog/products/data-analytics/agentic-analytics-with-the-data-agent-kit?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog). **Build a data science pipeline:** Try the [Fraud detection pipeline with Data Agent Kit and Antigravity IDE](https://codelabs.developers.google.com/dak-data-science-antigravity-ide?utm_campaign=CDR_0xaea1deef_default_b566338695&utm_medium=external&utm_source=blog) codelab.