How to Use the Google BigQuery MCP in CrewAI

Q: How do I add Google BigQuery to a CrewAI agent?

Pass the server URL in your agent's mcps list. This gives the crew instant access to the seven tools for querying and metadata inspection.

Deploy autonomous agent crews to manage and analyze Google BigQuery data.

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Connect Google BigQuery MCP to CrewAI

Create your Vinkius account to connect Google BigQuery to CrewAI 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 CrewAI

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Collaborative analysis in CrewAI crews

Assign one agent to `list_tables` and another to `execute_query` to split the labor. Your crew works together, with specialized agents handling different parts of the data pipeline. This division of labor keeps your agents focused. One agent gathers the necessary context, and the executor runs the final analysis, which prevents the crew from getting overwhelmed by large datasets.

Autonomous job monitoring in CrewAI

Set a monitor agent to watch jobs using `list_jobs`. If a critical report fails, the monitor can alert a human or trigger a secondary agent to investigate the logs. You create a self-managing system that handles its own errors. By delegating the monitoring work to an agent, you avoid manual oversight while maintaining high reliability in your data operations.

Dynamic schema discovery for CrewAI

Equip your researchers with `get_table` to investigate new datasets dynamically. They can discover schemas on the fly, allowing the crew to adapt to new data sources without you needing to update their instructions. This flexibility allows your agents to explore and analyze data independently. They learn the structure of the database as they work, making them effective even when the data layout changes.

Setup guide

Set up Google BigQuery MCP in CrewAI

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Google BigQuery tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Google BigQuery Analyst",
    goal="Access and analyze Google BigQuery data via MCP.",
    backstory="Expert analyst with direct Google BigQuery access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Google BigQuery transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Google BigQuery Analyst",
        goal="Access and analyze Google BigQuery data via MCP.",
        backstory="Expert analyst with direct Google BigQuery access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Google BigQuery transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 CrewAI

Pass the server URL in your agent's `mcps` list. This gives the crew instant access to the seven tools for querying and metadata inspection.

Yes, you can share the MCP connection across the entire crew. Each agent can call the tools independently to perform its specific role in the pipeline.

The server uses zero-trust principles. It only grants the agents access to the specific datasets you define in your configuration, keeping your project data isolated.

The server returns the BigQuery error message directly to the agent. The agent can then analyze the error, adjust the SQL, and try the query again.

The server enforces strict access controls at the tool level. It does not store your data; it acts as a secure, temporary bridge between your database and the agent's memory.

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