How to integrate Google BigQuery MCP with LangChain

This guide walks you through connecting Google BigQuery to LangChain 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 LangChain 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.

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Google BigQuery is a fully managed cloud data warehouse for fast SQL analytics on massive datasets. It's designed to help you analyze big data with ease, speed, and built-in machine learning.

63 Tools

Introduction

This guide walks you through connecting Google BigQuery to LangChain 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 LangChain 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

ChatGPTOpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenCodeCursorVS CodeOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Google BigQuery project to Composio
  • Create a Tool Router MCP session for Google BigQuery
  • Initialize an MCP client and retrieve Google BigQuery tools
  • Build a LangChain agent that can interact with Google BigQuery
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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.

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:

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

Step-by-step Guide

Step by step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
2

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.
3

Install dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Google BigQuery functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Google BigQuery tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['googlebigquery']
    }
);

const url = 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 session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Google BigQuery tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "googlebigquery-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Google BigQuery MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Google BigQuery tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Google BigQuery related question or task to the agent.\n");

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

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;
    }

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

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Google BigQuery and LangChain:

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['googlebigquery']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "googlebigquery-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Google BigQuery related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    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;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

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

Conclusion

You've successfully built a LangChain agent that can interact with Google BigQuery through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
TOOLS

Supported Tools

Every Google BigQuery action and event your agent gets out of the box.

Cancel BigQuery Job

Tool to cancel a running BigQuery job.

Create Capacity Commitment

Tool to create a new capacity commitment resource in BigQuery Reservation.

Create BigQuery Connection

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

Create Analytics Hub Data Exchange

Tool to create a new Analytics Hub data exchange for sharing BigQuery datasets.

Create Analytics Hub Listing

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

Create BigQuery Dataset

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

Create Analytics Hub Listing

Tool 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 Template

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

Create BigQuery Reservation

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

Create BigQuery Reservation Assignment

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

Create BigQuery Routine

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

Create BigQuery Table

Tool to create a new, empty table in a BigQuery dataset.

Delete BigQuery Dataset

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

Delete BigQuery Job Metadata

Tool to delete the metadata of a BigQuery job.

Delete BigQuery ML Model

Tool to delete a BigQuery ML model from a dataset.

Delete BigQuery Routine

Tool to delete a BigQuery routine by its ID.

Delete BigQuery Table

Tool to delete a BigQuery table from a dataset.

Get BigQuery ML Model

Tool to retrieve a specific BigQuery ML model resource by model ID.

Get BigQuery Connection IAM Policy

Tool to get the IAM access control policy for a BigQuery connection resource.

Get BigQuery Dataset Metadata

Tool to retrieve BigQuery dataset metadata including location via the datasets.

Get BigQuery Job

Tool to retrieve information about a specific BigQuery job.

Get BigQuery Query Results

Tool to get the results of a BigQuery query job via RPC.

Get BigQuery Routine

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

Get BigQuery Routine IAM Policy

Tool to retrieve the IAM access control policy for a BigQuery routine resource.

Get BigQuery Service Account

Tool to get the service account for a project used for interactions with Google Cloud KMS.

Get BigQuery Table IAM Policy

Tool to retrieve the IAM access control policy for a BigQuery table resource.

Get BigQuery Table Schema

Tool to fetch a BigQuery table's schema and metadata without querying row data.

Insert Data into BigQuery Table

Tool to stream data into BigQuery one record at a time without running a load job.

Insert BigQuery Job

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

Insert BigQuery Job with Upload

Tool to start a new BigQuery load job with file upload.

List Analytics Hub Listings

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

List BigQuery Connections

Tool to list BigQuery connections in a given project and location.

List BigQuery Capacity Commitments

Tool to list all capacity commitments for the admin project.

List Data Exchange Listings

Tool to list all listings in a given Analytics Hub data exchange using the v1beta1 API.

List BigQuery Datasets

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

List BigQuery Jobs

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

List BigQuery Data Transfer Locations

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

List Connections in Location

Tool to list BigQuery connections in a given project and location using the v1beta1 API.

List BigQuery Location Data Policies

Tool to list all data policies in a specified parent project and location using the v2beta1 API.

List BigQuery Models

Tool to list all BigQuery ML models in a specified dataset.

List Organization Data Exchanges

Tool to list all data exchanges from projects in a given organization and location using Analytics Hub API.

List BigQuery Projects

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

List Analytics Hub Query Templates

Tool to list all query templates in a given Analytics Hub data exchange.

List BigQuery Reservation Assignments

Tool to list BigQuery reservation assignments.

List BigQuery Reservation Groups

Tool to list all BigQuery reservation groups for a project in a specified location.

List BigQuery Reservations

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

List BigQuery Routines

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

List BigQuery Row Access Policies

Tool to list all row access policies on a specified BigQuery table.

List BigQuery Table Data

Tool to list the content of a BigQuery table in rows via the REST API.

List BigQuery Tables

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

Patch BigQuery Dataset

Tool to update an existing BigQuery dataset using RFC5789 PATCH semantics.

Patch BigQuery ML Model

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

Patch BigQuery Table

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

Query

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

Search All BigQuery Reservation Assignments

Tool to search all BigQuery reservation assignments for a specified resource in a particular region.

Set BigQuery Routine IAM Policy

Tool to set the IAM access control policy for a BigQuery routine resource.

Test BigQuery Routine IAM Permissions

Tool to test which IAM permissions the caller has on a BigQuery routine.

Undelete BigQuery Dataset

Tool to undelete a BigQuery dataset within the time travel window.

Update BigQuery Connection

Tool to update a specified BigQuery connection using the BigQuery Connection API.

Update BigQuery Dataset

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

Update BigQuery Routine

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

Update BigQuery Table

Tool to update an existing BigQuery table.

FRAMEWORKS

How to build Google BigQuery MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Google BigQuery MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Google BigQuery MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Google BigQuery MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Google BigQuery MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Google BigQuery MCP with Claude Cowork

Codex logo

Codex

Use Google BigQuery MCP with Codex

Cursor logo

Cursor

Use Google BigQuery MCP with Cursor

OpenClaw logo

OpenClaw

Use Google BigQuery MCP with OpenClaw

Hermes logo

Hermes

Use Google BigQuery MCP with Hermes

CLI logo

CLI

Use Google BigQuery MCP with CLI

Google ADK logo

Google ADK

Use Google BigQuery MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Google BigQuery MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Google BigQuery MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Google BigQuery MCP with LlamaIndex

CrewAI logo

CrewAI

Use Google BigQuery MCP with CrewAI

OpenCode logo

OpenCode

Use Google BigQuery MCP with OpenCode

VS Code logo

VS Code

Use Google BigQuery MCP with VS Code

FAQ

Frequently asked questions

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.

Yes, you can. LangChain 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.

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.

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.

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Co-founder, Slashy

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's immensely helped keep new users on the platform.

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Co-founder, Opennote

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

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CEO, Zams

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

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Founder, Extra Thursday

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

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Founder & CEO, SwarmZero

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.

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