How to Use the Looker (Business Intelligence & Data) MCP in CrewAI

Q: How do I configure Looker (Business Intelligence & Data) with CrewAI agents?

You pass the server URL directly into the agent's mcps parameter. This grants the entire crew access to the BI toolset for their collaborative tasks.

Q: Can CrewAI agents share Looker (Business Intelligence & Data) data?

Yes. The agents use shared memory to pass the results of runinlinequery between each other. This allows your research agent to feed data to your reporting agent seamlessly.

Deploy specialized BI agents for Looker (Business Intelligence & Data) using the CrewAI framework.

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Connect Looker (Business Intelligence & Data) MCP to CrewAI

Create your Vinkius account to connect Looker (Business Intelligence & Data) 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 Looker (Business Intelligence & Data) with CrewAI

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Specialized BI analysis crews

Assign agents to use `run_inline_query` for deep data investigation. You can have one agent run the query while another interprets the results for your team. This separation of concerns ensures that the data extraction remains precise. Your crew maintains a clear division between fetching metrics and drawing business conclusions.

Autonomous dashboard auditing

Task a monitor agent with `list_dashboards` and `get_dashboard` to keep an eye on your BI library health. It flags orphaned reports or inconsistent naming conventions automatically. Your agents can navigate the environment as a team, ensuring that every dashboard remains accessible and accurate. It keeps your BI stack tidy without manual intervention.

Cross-instance content discovery

Deploy a research agent to use `search_content` and `list_looks` for finding specific metrics across the entire platform. This agent maps the relationships between various data assets. It provides the rest of your crew with a clear picture of available data. By automating the search, you save your human analysts hours of manual navigation.

Setup guide

Set up Looker (Business Intelligence & Data) 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 Looker (Business Intelligence & Data) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Looker (Business Intelligence & Data) 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="Looker (Business Intelligence & Data) Analyst",
        goal="Access and analyze Looker (Business Intelligence & Data) data via MCP.",
        backstory="Expert analyst with direct Looker (Business Intelligence & Data) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Looker (Business Intelligence & Data) 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 Looker. 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 Looker (Business Intelligence & Data) MCP in CrewAI

You pass the server URL directly into the agent's `mcps` parameter. This grants the entire crew access to the BI toolset for their collaborative tasks.

Yes. The agents use shared memory to pass the results of `run_inline_query` between each other. This allows your research agent to feed data to your reporting agent seamlessly.

You can limit tool exposure by using a tool filter in your agent definition. This ensures only the authorized agents can execute queries against your BI environment.

They operate in a sequential or hierarchical structure defined in your crew configuration. You control exactly which agent calls which tool and in what order.

The server uses a zero-trust architecture. It restricts the agents to only the tools defined, and all traffic is encrypted, ensuring your dashboard query history remains strictly confidential.

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