How to Use the Google BigQuery MCP in Google ADK

Q: Can I filter the tools available to Google ADK for Google BigQuery?

You can use the toolnames filter in your configuration. This restricts the agent to only the operations you permit.

Connect your Google ADK agent to your data warehouse with this Google BigQuery MCP server.

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Connect Google BigQuery MCP to Google ADK

Create your Vinkius account to connect Google BigQuery to Google ADK 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 Google ADK

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Native SQL execution for Google ADK

Your agent runs `execute_query` to pull insights from BigQuery without leaving the Google ADK workflow. This creates a direct path from your data to your reasoning engine. You get the speed of BigQuery integrated into your agent's decision logic.

Schema discovery in Google ADK

Use `list_tables` and `get_table` to map out your datasets before running analysis. Your agent understands the data layout before it writes code. This reduces the risk of failed queries. You spend less on wasted bytes and more on accurate answers.

Manage BigQuery jobs via Google ADK

The `list_jobs` tool lets your agent check on the status of pending work. It's built for tracking complex data pipelines. It updates your agent on job progress in real-time. You don't have to monitor the console manually.

Setup guide

Set up Google BigQuery MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Google BigQuery tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Google BigQuery_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Google BigQuery tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Google BigQuery MCP in Google ADK

Yes, the server provides a standard transport that the ADK toolset consumes. Just point the toolset to your server URL.

By using the schema inspection tools first. The agent checks column names and types to write precise SQL instead of guessing.

You can use the tool_names filter in your configuration. This restricts the agent to only the operations you permit.

It supports standard HTTP and Stdio transports. This keeps communication snappy and responsive for your agent.

The server requires a valid endpoint token for every request. Your data remains isolated within your GCP project boundaries.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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