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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use Google BigQuery with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Empower your AI agent to query massive datasets via BigQuery. execute Standard SQL, track active jobs, and inspect table schemas natively.

Included with plan

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Complete set · 7 capabilities

The complete Google BigQuery capability set.

These are the exact actions your AI can choose when you ask it to work with Google BigQuery.

Capability set01 / 02

01-04

4 capabilities in this set.

Part of 7 available through Google BigQuery.

  1. 01

    Get table

    Get explicit metadata and schema details of a pure BigQuery Table

  2. 02

    List tables

    List explicit Tables natively contained within a Dataset

  3. 03

    Get job

    Get complete details of a specific BigQuery Job run

  4. 04

    List jobs

    List recent explicit BigQuery runtime Jobs securely

Capability set02 / 02

05-07

3 capabilities in this set.

Part of 7 available through Google BigQuery.

  1. 05

    Execute query

    Run an explicit BigQuery Standard SQL command

  2. 06

    Get dataset

    Get exact details of a specific BigQuery dataset

  3. 07

    List datasets

    List all explicit Datasets in the active GCP project

Observed, not estimated

840ms average. Fast in production.

Google BigQuery is checked daily against the live service.

Daily averagePeak 1025ms
Aug 20Today
Fastest day
688ms
Slowest day
1025ms
14-day trend
Slowing+16%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 7 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Google BigQuery, so you can see the experience inside your AI.

It does not authenticate your account with Google BigQuery. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

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

Step-by-step instructions for each client are in the guide. How to connect

FAQ

Questions Google BigQuery owners ask.

  • 01

    Can my AI write its own queries if I just ask it a business question?

    Yes! The agent will typically use list_tables and get_table to study the columns first. Then, realizing constraints, it will natively invoke execute_query running an optimized Standard SQL string to fetch exactly what you asked for.

  • 02

    Will my prompt fail if it returns millions of rows?

    It might hit the context window boundaries of the chosen foundational LLM. Good practice suggests instructing your AI to always append LIMIT 100 initially or run macro aggregations (like COUNT() or SUM()) natively inside BigQuery first.

  • 03

    How do I check if a query was expensive after it ran?

    Use the list_jobs or get_job endpoints. They expose metadata directly from Google's history returning the totalBytesProcessed flag so your agent can estimate overhead intelligently.

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