How to Use the Google BigQuery MCP in Pydantic AI

Get type-safe Google BigQuery data in your Pydantic AI agent using this MCP server.

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Connect Google BigQuery MCP to Pydantic AI

Create your Vinkius account to connect Google BigQuery to Pydantic AI 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 Pydantic AI

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Type-validated BigQuery access in Pydantic AI

Every response from `execute_query` hits your Pydantic models for immediate validation. If the data doesn't match your schema, the agent catches it instantly. This stops bad data from propagating through your logic. You get strict, predictable data handling.

Schema-first discovery for Pydantic AI

The agent calls `get_table` to inform its Pydantic models before querying. It ensures the SQL output is compatible with your type definitions. This tightens the loop between table schema and agent knowledge. You avoid runtime errors caused by unexpected data types.

Job oversight for Pydantic AI

Your agent uses `get_job` to verify completion before proceeding. It treats every BigQuery job as a discrete, trackable event. This makes your pipeline resilient. The agent knows exactly when data is ready for the next step.

Setup guide

Set up Google BigQuery MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "google-bigquery-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Google BigQuery tools.",
)

result = await agent.run("List recent Google BigQuery transactions")
print(result.output)

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Common questions about Google BigQuery MCP in Pydantic AI

It checks incoming data against your defined Pydantic models. Any field mismatch triggers a validation error instead of silent corruption.

Yes, by defining your models to match the table schema. Use `get_table` to inspect the structure before setting your types.

The server supports both SSE and HTTP transports. You can choose the one that fits your deployment strategy.

The agent receives a clear error message. You can catch these in your code to trigger retries or log the failure.

The server enforces strict access controls on every call. Only authorized credentials can touch your specific row-level records.

Use it with your favorite AI tools

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

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