How to integrate Google BigQuery MCP with LlamaIndex

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

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Introduction

This guide walks you through connecting Google BigQuery to LlamaIndex 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 LlamaIndex 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:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Google BigQuery
  • Connect LlamaIndex to the Google BigQuery MCP server
  • Build a Google BigQuery-powered agent using LlamaIndex
  • Interact with Google BigQuery through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built for async/await patterns

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 you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Google BigQuery account and project
  • Basic familiarity with async Python/Typescript
2

Getting API Keys for OpenAI, Composio, and Google BigQuery

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID
3

Installing dependencies

npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv

Create a new Typescript project and install the necessary dependencies:

  • @composio/llamaindex: Composio's LlamaIndex integration
  • @llamaindex/openai: OpenAI LLM integration
  • @llamaindex/tools: MCP client for LlamaIndex
  • @llamaindex/workflow: Workflow framework for LlamaIndex
  • dotenv: Environment variable management
4

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Google BigQuery access
5

Import modules

import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

Create a new file called google bigquery_llamaindex_agent.ts and import the required modules:

Key imports:

  • dotenv.config loads .env at runtime
  • readline gives us a simple CLI chat loop
  • Composio is the main Composio SDK client
  • mcp connects to an MCP endpoint
  • createAgent builds a LlamaIndex agent
  • openai configures the LLM backend
6

Load environment variables and initialize Composio

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

7

Create a Tool Router session and build the agent function

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["googlebigquery"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Google BigQuery actions." ,
    llm,
    tools,
  });

  return agent;
}

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, google bigquery)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Google BigQuery tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
8

Create an interactive chat loop

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

What's happening:

  • We're creating a direct terminal interface to chat with Google BigQuery
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • The agent processes the request, selects appropriate Google BigQuery tools, and returns a result
  • We extract the answer from the result data structure and display it to the user
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are streamed in a clear, readable format
9

Define the main entry point

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Google BigQuery
10

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Google BigQuery, then start asking questions.

Complete Code

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

import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["googlebigquery"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Google BigQuery actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();

Conclusion

You've successfully connected Google BigQuery to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Google BigQuery tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.
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

LangChain logo

LangChain

Use Google BigQuery MCP with LangChain

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

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. LlamaIndex 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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