How to Use the Google BigQuery MCP in Vercel AI SDK

Q: Can Vercel AI SDK query Google BigQuery directly?

Yes, by using the executequery tool. Your agent generates the SQL based on user input, sends it to the database, and streams the result back to your UI.

Stream real-time data from Google BigQuery directly into your Vercel AI SDK interface without the wait.

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Connect Google BigQuery MCP to Vercel AI SDK

Create your Vinkius account to connect Google BigQuery to Vercel AI SDK 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 Vercel AI SDK

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Live SQL execution in Vercel AI SDK

Run your standard SQL queries instantly using `execute_query`. The results stream straight to your frontend components, letting users see data flow as the agent processes the request. Stop relying on loading spinners for heavy data lifts. By connecting this MCP Server, your UI updates the moment the database returns a row, creating a faster experience for your end users.

Schema discovery for AI SDK agents

Use `get_table` and `list_tables` to let your agent inspect the database structure before writing code. This prevents runtime errors by ensuring the agent knows the exact column names and data types before it attempts a query. Your app stays stable because the agent works with actual metadata. It reads the schema first, then builds the query, which significantly cuts down on failed database requests during runtime.

Track job status in the browser

Monitor background processes by calling `get_job` or `list_jobs` within your application logic. This gives your users visibility into how long their requests take or if a specific process is still running. Integrating these tools means your agent can provide status updates to the user automatically. You get full transparency on every operation, keeping the user informed without extra manual checks.

Setup guide

Set up Google BigQuery MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Google BigQuery tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Google BigQuery transactions",
});

console.log(text);
await mcpClient.close();

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Google BigQuery. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Google BigQuery MCP in Vercel AI SDK

Install the MCP client package and configure your transport URL. Point the SDK toward the server endpoint to expose the seven available tools for your agent to use.

Yes, by using the `execute_query` tool. Your agent generates the SQL based on user input, sends it to the database, and streams the result back to your UI.

The server operates on a per-session basis. It handles authentication via your project tokens, ensuring that only your agent has access to the specific datasets it needs.

The server returns an error message through the tool output. You can use the `get_job` tool to inspect the failure logs and provide the user with a clear explanation.

It exposes only what you permit through your agent's configuration. The server restricts access to specific datasets, protecting your sensitive information while allowing the agent to perform necessary analysis.

Use it with your favorite AI tools

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