Google BigQuery with AutoGen

How to integrate Google BigQuery MCP with Autogen

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Introduction Also integrate Google BigQuery with TL;DR What is AutoGen 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 AutoGen 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 AutoGen 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

ChatGPT OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenCode Cursor VS Code OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

Get and set up your OpenAI and Composio API keys
Install the required dependencies for Autogen and Composio
Initialize Composio and create a Tool Router session for Google BigQuery
Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
Configure an Autogen AssistantAgent that can call Google BigQuery tools
Run a live chat loop where you ask the agent to perform Google BigQuery operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

Multi-Agent Systems: Build collaborative agent workflows
MCP Workbench: Native support for Model Context Protocol tools
Streaming HTTP: Connect to external services through streamable HTTP
AssistantAgent: Pre-built agent class for tool-using assistants

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

You will need:

A Composio API key
An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
A Google BigQuery account you can connect to Composio
Some basic familiarity with Autogen and Python async

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 dependencies

bash

pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

composio connects your agent to Google BigQuery via MCP
autogen-agentchat provides the AssistantAgent class
autogen-ext-openai provides the OpenAI model client
autogen-ext-tools provides MCP workbench support

Set up environment variables

bash

COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

COMPOSIO_API_KEY is required to talk to Composio
OPENAI_API_KEY is used by Autogen's OpenAI client
USER_ID is how Composio identifies which user's Google BigQuery connections to use

Import dependencies and create Tool Router session

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Google BigQuery session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["googlebigquery"]
    )
    url = session.mcp.url

What's happening:

load_dotenv() reads your .env file
Composio(api_key=...) initializes the SDK
create(...) creates a Tool Router session that exposes Google BigQuery tools
session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python

# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

url points to the Tool Router MCP endpoint from Composio
timeout is the HTTP timeout for requests
sse_read_timeout controls how long to wait when streaming responses
terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python

# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Google BigQuery assistant agent with MCP tools
    agent = AssistantAgent(
        name="googlebigquery_assistant",
        description="An AI assistant that helps with Google BigQuery operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

OpenAIChatCompletionClient wraps the OpenAI model for Autogen
McpWorkbench connects the agent to the MCP tools
AssistantAgent is configured with the Google BigQuery tools from the workbench

Run the interactive chat loop

python

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

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

What's happening:

The script prompts you in a loop with You:
Autogen passes your input to the model, which decides which Google BigQuery tools to call via MCP
agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
Typing exit, quit, or bye ends the loop

Complete Code

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

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Google BigQuery session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["googlebigquery"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Google BigQuery assistant agent with MCP tools
        agent = AssistantAgent(
            name="googlebigquery_assistant",
            description="An AI assistant that helps with Google BigQuery operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Google BigQuery through Composio's Tool Router and MCP. From here you can:

Add more toolkits to the toolkits list, for example notion or hubspot
Refine the agent description to point it at specific workflows
Wrap this script behind a UI, Slack bot, or internal tool

Once the pattern is clear for Google BigQuery, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Google BigQuery MCP Agent with another framework

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CLI

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Google ADK

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VS Code

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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 Autogen?

Yes, you can. Autogen 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 Autogen

Table of Contents

Connect Google BigQuery without Auth hassles

Introduction

Also integrate Google BigQuery with

TL;DR

What is AutoGen?

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 dependencies

Set up environment variables

Import dependencies and create Tool Router session

Configure MCP parameters for Autogen

Create the model client and agent

Run the interactive chat loop

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

Vercel AI SDK

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 Autogen?

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