How to Use the Google BigQuery MCP in OpenAI Agents SDK

Query your data directly from your OpenAI Agents SDK production environment with this MCP server.

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Connect Google BigQuery MCP to OpenAI Agents SDK

Create your Vinkius account to connect Google BigQuery to OpenAI Agents SDK 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 OpenAI Agents SDK

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Execute SQL with OpenAI Agents SDK

Your agent runs complex data operations using `execute_query`. It bypasses manual exports by hitting your warehouse directly. This keeps your data pipeline inside the agent lifecycle. You maintain full control over the query execution and result parsing.

Inspect schema metadata in OpenAI Agents SDK

The agent fetches table structures using `get_table` before writing any SQL. This prevents syntax errors and saves on bytes billed. It verifies the column types and constraints first. Your agent only runs queries that align with your actual BigQuery table architecture.

Track BigQuery job status

Monitor your long-running processes with `list_jobs` and `get_job`. You get immediate visibility into execution state and performance. This allows your agent to handle retries or timeouts automatically. You stop guessing if a process finished and start acting on the results.

Setup guide

Set up Google BigQuery MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Google BigQuery tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Google BigQuery tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Google BigQuery tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Google BigQuery Agent",
            instructions="You have access to Google BigQuery tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Google BigQuery MCP in OpenAI Agents SDK

You instantiate the server via an HTTP endpoint and pass it to your agent constructor. The SDK handles tool discovery automatically.

Yes, but you should keep result sets within memory limits. Use specific SQL filters to avoid pulling unnecessary data into your agent state.

Only if your service account permissions allow it. The toolset executes standard SQL, so any DDL or DML is subject to your cloud IAM settings.

The server operates over encrypted channels. It only touches the data you explicitly query through the provided toolset.

Security relies on IAM roles. The server only sees the datasets and tables your credentials can access, ensuring no unauthorized data exposure.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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