How to Use the Cube.dev MCP in CrewAI

Q: How do I connect CrewAI to Cube.dev?

Pass your Vinkius endpoint directly into the mcps array on your agent. Use pip install crewaitools to grab the dependencies.

Q: Can I restrict which Cube.dev tools my CrewAI agents see?

Yes. Import MCPServerHTTP and apply a toolfilter. You probably want your analyst agent seeing queries, not deployment tools.

Deploy autonomous agent teams that monitor, debug, and query your semantic layer using CrewAI and this MCP Server.

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Connect Cube.dev MCP to CrewAI

Create your Vinkius account to connect Cube.dev 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 Cube.dev with CrewAI

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Multi-Agent Data Analysis

`load_query` fetches aggregated metrics from your data models. You assign this tool to a dedicated analyst agent inside your CrewAI setup. That analyst passes the raw numbers to a separate reporting agent. The reporting agent formats the output and emails stakeholders without human intervention.

CrewAI Deployment Monitoring

`list_deployments` returns every active Cube Cloud instance attached to your account. A monitor agent watches these environments around the clock using this MCP Server. If a specific instance fails, the crew spots the issue. They can pull connection details via `list_environments` and escalate the incident to your DevOps team.

Autonomous Schema Discovery

`get_entity` pulls granular details about specific cubes and views. Your planning agent uses this MCP Server to understand what data actually exists before writing queries. Knowing the exact schema prevents hallucinated column names. The crew maps out the relationships using `get_meta` and builds accurate reports on the first try.

Setup guide

Set up Cube.dev 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 Cube.dev tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Cube.dev 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="Cube.dev Analyst",
        goal="Access and analyze Cube.dev data via MCP.",
        backstory="Expert analyst with direct Cube.dev access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Cube.dev 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 Cube.dev. 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 Cube.dev MCP in CrewAI

Pass your Vinkius endpoint directly into the `mcps` array on your agent. Use `pip install crewai[tools]` to grab the dependencies.

Yes. Import `MCPServerHTTP` and apply a `tool_filter`. You probably want your analyst agent seeing queries, not deployment tools.

CrewAI manages the Python side while Vinkius handles the isolated server execution. You just provide the endpoint token.

No. The MCP Server processes concurrent requests cleanly. Your crew can run parallel research tasks without stepping on each other.

Agents read dimension metadata, data source lists, and query outputs. We isolate those payloads in an ephemeral Vinkius sandbox that wipes completely clean after the Python script terminates.

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