How to Use the Hevo Data (ETL & Data Pipeline) MCP in CrewAI

Q: How do I configure CrewAI to use Hevo Data (ETL & Data Pipeline)?

Install crewaitools and pass your Vinkius endpoint URL directly into the mcps array on your Agent definition. CrewAI handles the HTTP transport and tool discovery automatically.

Q: How do I filter which Hevo Data (ETL & Data Pipeline) tools CrewAI can see?

Use MCPServerHTTP from crewai.mcp and apply a toolfilter. This lets you expose listpipelines to a junior monitoring agent while hiding the usage metrics.

Deploy autonomous CrewAI agents to monitor your Hevo data pipelines.

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Connect Hevo Data (ETL & Data Pipeline) MCP to CrewAI

Create your Vinkius account to connect Hevo Data (ETL & Data Pipeline) 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 Hevo Data (ETL & Data Pipeline) with CrewAI

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Assign Pipeline Monitors

`list_pipelines` and `get_pipeline` equip your CrewAI monitoring agent with direct access to your ETL sync statuses. You assign one agent the specific role of watching data flows. This agent queries the Hevo API continuously. When it detects a stalled sync, it passes the failure details in shared memory to a senior analyst agent for investigation.

Delegate Usage Audits via MCP Server

`get_usage` lets your financial agent track Hevo event consumption without human input. You pass the MCP Server URL directly into the agent's configuration array. The agent checks the billing metrics daily and compares them against historical data. If the event count spikes, the agent alerts your DevOps crew before the invoice arrives.

Map Workflows and Destinations

`list_workflows`, `list_models`, and `list_destinations` give your architecture agent a complete map of your data infrastructure. The agent reads the warehouse targets and transformation logic directly from Hevo. Your CrewAI team can run a sequential process where one agent maps the destinations, and another verifies the models. They operate autonomously to audit the entire data pipeline setup.

Setup guide

Set up Hevo Data (ETL & Data Pipeline) 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 Hevo Data (ETL & Data Pipeline) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Hevo Data (ETL & Data Pipeline) 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 Hevo Data. 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 Hevo Data (ETL & Data Pipeline) MCP in CrewAI

Install `crewai[tools]` and pass your Vinkius endpoint URL directly into the `mcps` array on your Agent definition. CrewAI handles the HTTP transport and tool discovery automatically.

Yes. You can attach the same client to multiple agents in your crew. However, it is usually better to give one specialized agent the Hevo tools and let it share the results in memory.

Use `MCPServerHTTP` from `crewai.mcp` and apply a `tool_filter`. This lets you expose `list_pipelines` to a junior monitoring agent while hiding the usage metrics.

You can set up a manager agent that delegates Hevo queries to subordinate agents. The manager decides when to check destinations and assigns the task down the chain.

The Vinkius endpoint processes the request inside a stateless, isolated sandbox. Your agents receive the workflow definitions and pipeline IDs, but the server tears down the connection immediately. We keep zero records of your data architecture.

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