How to Use the Google BigQuery MCP in Mastra AI

Q: Does Google BigQuery require human approval in Mastra AI?

You can toggle the requireToolApproval setting in your Mastra config. This forces the agent to ask for your permission before it runs any query.

Build resilient data workflows in Mastra AI using direct Google BigQuery integration.

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Connect Google BigQuery MCP to Mastra AI

Create your Vinkius account to connect Google BigQuery to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Google BigQuery with Mastra AI

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Automated data workflows with Mastra AI

Chain your logic by combining `list_datasets` and `execute_query` within a Mastra agent. Your workflow can automatically identify relevant data, run the analysis, and move to the next step without human intervention. This setup handles complex tasks by ensuring each tool call provides the input for the next. If a step requires specific schema knowledge, the agent fetches it using `get_table` before proceeding.

Reliable job tracking for Mastra AI agents

Use `list_jobs` to monitor the lifecycle of your database operations. Mastra can automatically trigger retries or error alerts if a job takes too long or fails to complete. Your agents become self-healing by checking the status of their own requests. This ensures that long-running data pipelines don't just hang when a query encounters an issue.

Precise schema inspection for Mastra AI

Call `get_dataset` to understand the scope of your data before performing operations. This metadata-first approach ensures your agents generate accurate, valid SQL every time they interact with the database. By verifying the environment before execution, you save costs on malformed queries. Your agent gains a clear understanding of the data architecture, which leads to better decision-making in your workflows.

Setup guide

Set up Google BigQuery MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Google BigQuery tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "google-bigquery-mcp-client",
  servers: {
    "google-bigquery-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Google BigQuery Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Google BigQuery tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Google BigQuery transactions"
);
console.log(result.text);

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Common questions about Google BigQuery MCP in Mastra AI

Mastra treats these as native agent tools. You spread the tools into your agent definition, allowing the agent to call them whenever the current step requires database interaction.

Absolutely. You can define a sequence where the agent lists tables, inspects a schema, and executes a query as part of a single, automated task.

You can toggle the `requireToolApproval` setting in your Mastra config. This forces the agent to ask for your permission before it runs any query.

The server uses your project credentials to establish a secure connection. You keep your keys in your environment variables, and the server manages the handshake.

The server uses ephemeral sessions. It only accesses the data you explicitly query, and it clears all transient metadata once the agent completes its current task.

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