Google BigQuery with Vercel AI SDK

How to integrate Google BigQuery MCP with Vercel AI SDK v6

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Introduction Also integrate Google BigQuery with TL;DR What is Vercel AI SDK What is the Google BigQuery MCP server Supported Tools & Triggers Creating MCP Server - Stand-alone vs Composio SDK Step-by-step Guide Complete Code Conclusion How to build Google BigQuery MCP Agent with another framework Explore Other Toolkits FAQ

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Introduction

This guide walks you through connecting Google BigQuery to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Google BigQuery agent that can run yesterday's sales summary query, find top 10 customers by revenue, analyze traffic data for last quarter through natural language commands.

This guide will help you understand how to give your Vercel AI SDK agent real control over a Google BigQuery account through Composio's Google BigQuery MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Google BigQuery with

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TL;DR

Here's what you'll learn:

How to set up and configure a Vercel AI SDK agent with Google BigQuery integration
Using Composio's Tool Router to dynamically load and access Google BigQuery tools
Creating an MCP client connection using HTTP transport
Building an interactive CLI chat interface with conversation history management
Handling tool calls and results within the Vercel AI SDK framework

What is Vercel AI SDK?

The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state.

Key features include:

streamText: Core function for streaming responses with real-time tool support
MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp
Step Counting: Control multi-step tool execution with stopWhen: stepCountIs()
OpenAI Provider: Native integration with OpenAI models

What is the Google BigQuery MCP server, and what's possible with it?

The Google BigQuery MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Google BigQuery account. It provides structured and secure access to your data warehouse, so your agent can perform actions like running SQL queries, analyzing datasets, extracting insights, and automating reporting on your behalf.

Instant SQL query execution: Have your agent run complex analytical queries on any of your BigQuery datasets and get results in real time.
Custom data analysis and reporting: Instruct your agent to generate summaries, trends, or statistics by querying specific tables or views.
Automated data extraction: Let your agent fetch and transform data for integration with other tools or for further analysis.
Interactive business intelligence: Enable your agent to answer ad hoc data questions, visualize aggregated data, or pull specific metrics from massive datasets instantly.
Streamlined workflow automation: Use your agent to automate recurring BigQuery tasks, such as daily audits or data slice generation, without manual effort.

Supported Tools & Triggers

Tools

Cancel BigQuery JobTool to cancel a running BigQuery job.

Create Capacity CommitmentTool to create a new capacity commitment resource in BigQuery Reservation.

Create BigQuery ConnectionTool to create a new BigQuery connection to external data sources using the BigQuery Connection API.

Create Analytics Hub Data ExchangeTool to create a new Analytics Hub data exchange for sharing BigQuery datasets.

Create Analytics Hub ListingTool to create a new listing in a BigQuery Analytics Hub data exchange.

Create BigQuery DatasetTool to create a new BigQuery dataset with explicit location, labels, and description using the BigQuery Datasets API.

Create Analytics Hub ListingTool to create a new listing in a data exchange using Analytics Hub API.

Create BigQuery Data Policy (v2beta1)Tool to create a new data policy under a project with specified location using the v2beta1 BigQuery Data Policy API.

Create Analytics Hub Query TemplateTool to create a new query template in a BigQuery Analytics Hub Data Clean Room (DCR) data exchange.

Create BigQuery ReservationTool to create a new BigQuery reservation resource to guarantee compute capacity (slots) for query and pipeline jobs.

Create BigQuery Reservation AssignmentTool to create a BigQuery reservation assignment that allows a project, folder, or organization to submit jobs using slots from a specified reservation.

Create BigQuery RoutineTool to create a new user-defined routine (function or procedure) in a BigQuery dataset.

Create BigQuery TableTool to create a new, empty table in a BigQuery dataset.

Delete BigQuery DatasetTool to delete a BigQuery dataset specified by datasetId via the datasets.

Delete BigQuery Job MetadataTool to delete the metadata of a BigQuery job.

Delete BigQuery ML ModelTool to delete a BigQuery ML model from a dataset.

Delete BigQuery RoutineTool to delete a BigQuery routine by its ID.

Delete BigQuery TableTool to delete a BigQuery table from a dataset.

Get BigQuery ML ModelTool to retrieve a specific BigQuery ML model resource by model ID.

Get BigQuery Connection IAM PolicyTool to get the IAM access control policy for a BigQuery connection resource.

Get BigQuery Dataset MetadataTool to retrieve BigQuery dataset metadata including location via the datasets.

Get BigQuery JobTool to retrieve information about a specific BigQuery job.

Get BigQuery Query ResultsTool to get the results of a BigQuery query job via RPC.

Get BigQuery RoutineTool to retrieve a BigQuery routine (user-defined function or stored procedure) by its ID.

Get BigQuery Routine IAM PolicyTool to retrieve the IAM access control policy for a BigQuery routine resource.

Get BigQuery Service AccountTool to get the service account for a project used for interactions with Google Cloud KMS.

Get BigQuery Table IAM PolicyTool to retrieve the IAM access control policy for a BigQuery table resource.

Get BigQuery Table SchemaTool to fetch a BigQuery table's schema and metadata without querying row data.

Insert Data into BigQuery TableTool to stream data into BigQuery one record at a time without running a load job.

Insert BigQuery JobTool to start a new asynchronous BigQuery job (query, load, extract, or copy).

Insert BigQuery Job with UploadTool to start a new BigQuery load job with file upload.

List Analytics Hub ListingsTool to list all listings in a given Analytics Hub data exchange.

List BigQuery ConnectionsTool to list BigQuery connections in a given project and location.

List BigQuery Capacity CommitmentsTool to list all capacity commitments for the admin project.

List Data Exchange ListingsTool to list all listings in a given Analytics Hub data exchange using the v1beta1 API.

List BigQuery DatasetsTool to list datasets in a specific BigQuery project, including dataset locations.

List BigQuery JobsTool to list all jobs that you started in a BigQuery project.

List BigQuery Data Transfer LocationsTool to list information about supported locations for BigQuery Data Transfer Service.

List Connections in LocationTool to list BigQuery connections in a given project and location using the v1beta1 API.

List BigQuery Location Data PoliciesTool to list all data policies in a specified parent project and location using the v2beta1 API.

List BigQuery ModelsTool to list all BigQuery ML models in a specified dataset.

List Organization Data ExchangesTool to list all data exchanges from projects in a given organization and location using Analytics Hub API.

List BigQuery ProjectsTool to list BigQuery projects to which the user has been granted any project role.

List Analytics Hub Query TemplatesTool to list all query templates in a given Analytics Hub data exchange.

List BigQuery Reservation AssignmentsTool to list BigQuery reservation assignments.

List BigQuery Reservation GroupsTool to list all BigQuery reservation groups for a project in a specified location.

List BigQuery ReservationsTool to list all BigQuery reservations for a project in a specified location.

List BigQuery RoutinesTool to list all routines (user-defined functions and stored procedures) in a BigQuery dataset.

List BigQuery Row Access PoliciesTool to list all row access policies on a specified BigQuery table.

List BigQuery Table DataTool to list the content of a BigQuery table in rows via the REST API.

List BigQuery TablesTool to list tables in a BigQuery dataset via the REST API.

Patch BigQuery DatasetTool to update an existing BigQuery dataset using RFC5789 PATCH semantics.

Patch BigQuery ML ModelTool to update specific fields in an existing BigQuery ML model using PATCH semantics.

Patch BigQuery TableTool to update specific fields in an existing BigQuery table using RFC5789 PATCH semantics.

QueryQuery Tool runs a SQL query in BigQuery using the REST API.

Search All BigQuery Reservation AssignmentsTool to search all BigQuery reservation assignments for a specified resource in a particular region.

Set BigQuery Routine IAM PolicyTool to set the IAM access control policy for a BigQuery routine resource.

Test BigQuery Routine IAM PermissionsTool to test which IAM permissions the caller has on a BigQuery routine.

Undelete BigQuery DatasetTool to undelete a BigQuery dataset within the time travel window.

Update BigQuery ConnectionTool to update a specified BigQuery connection using the BigQuery Connection API.

Update BigQuery DatasetTool to update information in an existing BigQuery dataset using the PUT method.

Update BigQuery RoutineTool to update an existing BigQuery routine (function or stored procedure).

Update BigQuery TableTool to update an existing BigQuery table.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before you begin, make sure you have:

Node.js and npm installed
A Composio account with API key
An OpenAI API key

Getting API Keys for OpenAI and Composio

OpenAI API Key

Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
Keep the API key safe.

Composio API Key

Log in to the Composio dashboard.
Navigate to your API settings and generate a new API key.
Store this key securely as you'll need it for authentication.

Install required dependencies

bash

npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv

First, install the necessary packages for your project.

What you're installing:

@ai-sdk/openai: Vercel AI SDK's OpenAI provider
@ai-sdk/mcp: MCP client for Vercel AI SDK
@composio/core: Composio SDK for tool integration
ai: Core Vercel AI SDK
dotenv: Environment variable management

Set up environment variables

bash

OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here

Create a .env file in your project root.

What's needed:

OPENAI_API_KEY: Your OpenAI API key for GPT model access
COMPOSIO_API_KEY: Your Composio API key for tool access
COMPOSIO_USER_ID: A unique identifier for the user session

Import required modules and validate environment

typescript

import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});

What's happening:

We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio
The dotenv/config import automatically loads environment variables
The MCP client import enables connection to Composio's tool server

Create Tool Router session and initialize MCP client

typescript

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["googlebigquery"],
  });

  const mcpUrl = session.mcp.url;

What's happening:

We're creating a Tool Router session that gives your agent access to Google BigQuery tools
The create method takes the user ID and specifies which toolkits should be available
The returned mcp object contains the URL and authentication headers needed to connect to the MCP server
This session provides access to all Google BigQuery-related tools through the MCP protocol

Connect to MCP server and retrieve tools

typescript

const mcpClient = await createMCPClient({
  transport: {
    type: "http",
    url: mcpUrl,
    headers: session.mcp.headers, // Authentication headers for the Composio MCP server
  },
});

const tools = await mcpClient.tools();

What's happening:

We're creating an MCP client that connects to our Composio Tool Router session via HTTP
The mcp.url provides the endpoint, and mcp.headers contains authentication credentials
The type: "http" is important - Composio requires HTTP transport
tools() retrieves all available Google BigQuery tools that the agent can use

Initialize conversation and CLI interface

typescript

let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to googlebigquery, like summarize my last 5 emails, send an email, etc... :)))\n",
);

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

What's happening:

We initialize an empty messages array to maintain conversation history
A readline interface is created to accept user input from the command line
Instructions are displayed to guide the user on how to interact with the agent

Handle user input and stream responses with real-time tool feedback

typescript

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({ role: "user", content: trimmedInput });
  console.log("\nAgent is thinking...\n");

  try {
    const stream = streamText({
      model: openai("gpt-5"),
      messages,
      tools,
      toolChoice: "auto",
      stopWhen: stepCountIs(10),
      onStepFinish: (step) => {
        for (const toolCall of step.toolCalls) {
          console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});

What's happening:

We use streamText instead of generateText to stream responses in real-time
toolChoice: "auto" allows the model to decide when to use Google BigQuery tools
stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations
onStepFinish callback displays which tools are being used in real-time
We iterate through the text stream to create a typewriter effect as the agent responds
The complete response is added to conversation history to maintain context
Errors are caught and displayed with helpful retry suggestions

Complete Code

Here's the complete code to get you started with Google BigQuery and Vercel AI SDK:

typescript

import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["googlebigquery"],
  });

  const mcpUrl = session.mcp.url;

  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      url: mcpUrl,
      headers: session.mcp.headers, // Authentication headers for the Composio MCP server
    },
  });

  const tools = await mcpClient.tools();

  let messages: ModelMessage[] = [];

  console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
  console.log(
    "Ask any questions related to googlebigquery, like summarize my last 5 emails, send an email, etc... :)))\n",
  );

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
      console.log("\nGoodbye!");
      rl.close();
      process.exit(0);
    }

    if (!trimmedInput) {
      rl.prompt();
      return;
    }

    messages.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    try {
      const stream = streamText({
        model: openai("gpt-5"),
        messages,
        tools,
        toolChoice: "auto",
        stopWhen: stepCountIs(10),
        onStepFinish: (step) => {
          for (const toolCall of step.toolCalls) {
            console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});

Conclusion

You've successfully built a Google BigQuery agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback.

Key features of this implementation:

Real-time streaming responses for a better user experience with typewriter effect
Live tool execution feedback showing which tools are being used as the agent works
Dynamic tool loading through Composio's Tool Router with secure authentication
Multi-step tool execution with configurable step limits (up to 10 steps)
Comprehensive error handling for robust agent execution
Conversation history maintenance for context-aware responses

You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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TOOLKIT MARKETPLACE

FAQ

What are the differences in Tool Router MCP and Google BigQuery MCP?

With a standalone Google BigQuery MCP server, the agents and LLMs can only access a fixed set of Google BigQuery tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Google BigQuery and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Vercel AI SDK v6?

Yes, you can. Vercel AI SDK v6 fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Google BigQuery tools.

Can I manage the permissions and scopes for Google BigQuery while using Tool Router?

Yes, absolutely. You can configure which Google BigQuery scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Google BigQuery data and credentials are handled as safely as possible.

Used by agents from

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We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Get started for free Get a demo↗

Harsha GaddipatiCo-founder, Slashy

Karan skipped his own birthday party to fix our critical issue. It was 10 pm and he diverted his Waymo to help us instead. This really sets the bar, shows you the commitment you need to have when users rely on your software.

Abhi AryaCo-founder, Opennote

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it has immensely helped in keeping new users on the platform.

Nirman DaveCEO, Zams

We chose Composio over Pipedream because it delivered depth where it mattered. It supported niche tools and tricky edge cases that other platforms simply ignored. Giving us confidence to scale without compromising.

Ryan YuFounder, Extra Thursday

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down in the complexities of managing agent auth would have been a death sentence for Extra Thursday.

Tomisin JenrolaFounder & CEO, SwarmZero

Before partnering with Composio, adding tool integrations was a slow, resource-intensive process. Each integration could take weeks or months of engineering time, and maintaining them meant constantly keeping up with API changes.

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

How to integrate Google BigQuery MCP with Vercel AI SDK v6

Table of Contents

Connect Google BigQuery without Auth hassles

Introduction

Also integrate Google BigQuery with

TL;DR

What is Vercel AI SDK?

What is the Google BigQuery MCP server, and what's possible with it?

Supported Tools & Triggers

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install required dependencies

Set up environment variables

Import required modules and validate environment

Create Tool Router session and initialize MCP client

Connect to MCP server and retrieve tools

Initialize conversation and CLI interface

Handle user input and stream responses with real-time tool feedback

Complete Code

Conclusion

How to build Google BigQuery MCP Agent with another framework

ChatGPT

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

Cursor

OpenClaw

Hermes

CLI

Google ADK

LangChain

Mastra AI

LlamaIndex

CrewAI

OpenCode

VS Code

Explore Other Toolkits

Firecrawl

Tavily

Exa

FAQ

What are the differences in Tool Router MCP and Google BigQuery MCP?

Can I use Tool Router MCP with Vercel AI SDK v6?

Can I manage the permissions and scopes for Google BigQuery while using Tool Router?

How safe is my data with Composio Tool Router?

Used by agents from

Never worry about agent reliability