• TOPOLOGY
  • SCHEMA
  • CONFIG
  • HUMAN

How to inspect BigQuery table schemas with your AI

Connect once, then let your agent crawl your BigQuery environment. It will map out your data structures and settings automatically.

Ask AI about this page

Short answer

How can I use my AI to inspect BigQuery schemas?

Your agent uses this MCP to pull metadata directly from your BigQuery project. It can list datasets, find tables, and extract specific column details and partitioning settings. You'll get a clear view of your data topology without writing SQL.

The outcomes

What your AI uncovers

The AI pulls raw metadata and organizes it into clear structural maps.

TOPOLOGY

Dataset maps

The AI crawls your project to find all available datasets and the tables inside them. You get a full view of your data hierarchy.

SCHEMA

Column definitions

Your agent pulls the exact field names, data types, and modes for every column. This makes it easy to prep for downstream tasks.

CONFIG

Partitioning details

The AI identifies how your tables are partitioned or clustered. You'll know exactly how the data is organized for performance.

HUMAN

Manual validation

The AI provides the raw schema, but you decide if the structure meets your specific business logic or modeling needs.

The workflow

What your AI does when the request arrives.

The AI moves through your BigQuery hierarchy to gather the specific metadata you need.

  1. Locate datasets

    The AI starts by finding all datasets available in your connected BigQuery project.

    list_datasets

  2. Identify tables

    Once it finds the right dataset, the AI lists every table contained within it.

    list_tables

  3. Fetch dataset info

    The AI pulls high-level metadata about the dataset itself to understand its scope.

    get_dataset

  4. Extract table schema

    The AI drills into a specific table to pull the full list of columns, types, and partitioning settings.

    get_table

Try it

Copy these to start.

Use these prompts to begin your inspection.

Starting points

These are opening lines. Swap out the dataset or table names with your actual BigQuery identifiers.

Google BigQuery Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_XL1CHYzMkCvLxlGLPBzLHDOTg35sqGtg0BPtLGTY/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Google BigQuery capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "google-bigquery-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_XL1CHYzMkCvLxlGLPBzLHDOTg35sqGtg0BPtLGTY/mcp"
    }
  }
}
Copy into chat04

  • List all the datasets in my BigQuery project so I can see what's available.

  • Show me all the tables inside the 'sales_data' dataset.

  • What is the schema for the 'user_logs' table in my 'analytics' dataset?

  • Check the partitioning settings for the 'transactions' table.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect Google BigQuery once, then ask.

One connection gives your AI access to your BigQuery metadata. Your credentials stay encrypted, and you can start asking questions immediately.

Connect Google BigQuery to your AI

FAQ

How this task behaves

  • 01

    Can my AI edit or delete BigQuery tables?

    No. This MCP only provides tools to read and list metadata. It cannot modify, delete, or create any data or tables.

  • 02

    Can the AI run SQL queries against my data?

    No. This specific task is for inspecting schemas and structures. It does not have the capability to execute SQL commands.

  • 03

    Does the AI see the actual rows of data in my tables?

    No. The AI only sees the schema, column names, and structural settings. It does not access the actual data records within the rows.

  • 04

    How does the AI know which project to look at?

    The AI uses the project context provided during the connection process to locate your datasets.

  • 05

    Can I use this to find out which columns are partitioned?

    Yes. By pulling the table metadata, the AI can identify partitioning and clustering configurations.

  • More questions about Google BigQuery? The Connector page answers them. See everything the Google BigQuery Connector can do

Connect Google BigQuery to Claude, Cursor, ChatGPT & more