How to Use the Google BigQuery MCP in AutoGen

Q: How do AutoGen agents execute SQL using Google BigQuery?

You pass the executequery tool to your database agent's tool list. The agent uses this tool to run standard SQL statements whenever the conversation requires warehouse data.

Q: Can my AutoGen group discover new tables dynamically?

Yes, your agents can use listtables to see what is available in a dataset. They can then use gettable to pull the exact columns and plan their joint query strategy.

Q: How do I manage long query runtimes in Google BigQuery with AutoGen?

Your supervisor agent can use getjob to track the query's progress. While the job runs, other agents can continue discussing or processing other tasks.

Q: Who has access to my query history when using Google BigQuery with AutoGen?

Only your local agents can access the query logs via listjobs. Vinkius handles the connection through an ephemeral, zero-trust sandbox that leaves no trace of your query history or data.

Let your AutoGen agents debate query strategies and execute SQL directly on your database warehouse with this MCP Server.

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Connect Google BigQuery MCP to AutoGen

Create your Vinkius account to connect Google BigQuery to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Google BigQuery with AutoGen

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Collaborative SQL Execution with AutoGen

The `execute_query` tool allows your specialized AutoGen database agent to run SQL commands generated during multi-agent planning. This MCP Server setup lets one agent draft the query, a critic agent review it, and the execution agent run it. This collaborative loop prevents costly run errors on large datasets. The results are shared back to the group for joint analysis and decision-making.

Multi-Agent Schema Validation

The `get_table` tool provides raw schema details to your planning agents before they write complex joins. Your data engineer agent uses this tool to inspect column constraints and data types. By pairing this with `get_dataset`, the agents verify table relationships autonomously. They resolve schema mismatches during their conversation phase, before hitting the billing API.

Autonomous Job Monitoring

The `list_jobs` tool lets your coordinator agent track active queries across your workspace. If a query takes too long, a supervisor agent can check its status using `get_job`. This keeps your multi-agent system from getting blocked by heavy analytical tasks. The agents can pivot to other tasks while waiting for the query job to finish.

Setup guide

Set up Google BigQuery MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Google BigQuery tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Google BigQuery_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Google BigQuery data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

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

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Google BigQuery_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Google BigQuery data")
    print(result.messages[-1].content)

Get your connection token →

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

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Common questions about Google BigQuery MCP in AutoGen

You pass the `execute_query` tool to your database agent's tool list. The agent uses this tool to run standard SQL statements whenever the conversation requires warehouse data.

Yes, your agents can use `list_tables` to see what is available in a dataset. They can then use `get_table` to pull the exact columns and plan their joint query strategy.

Your supervisor agent can use `get_job` to track the query's progress. While the job runs, other agents can continue discussing or processing other tasks.

This server standardizes the database interface into seven clean, tested tools. You don't have to write, test, or secure custom Google Cloud API code inside your agent definitions.

Only your local agents can access the query logs via `list_jobs`. Vinkius handles the connection through an ephemeral, zero-trust sandbox that leaves no trace of your query history or data.

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