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

Q: Can LlamaIndex search my historical Hevo Data (ETL & Data Pipeline) usage?

Yes. If you periodically run the getusage tool and index the results into your vector store, your agent can perform semantic queries against that historical data.

Q: Can I filter which tools the agent indexes?

Absolutely. You can pass an allowedtools filter to the spec. If you only want the agent indexing destinations, restrict it to the listdestinations tool.

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

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

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Build a RAG index of your Hevo MCP Server

You have dozens of data syncs and no one remembers how they are configured. Connect this MCP Server to LlamaIndex and run `list_pipelines` alongside `list_destinations`. The framework takes those raw JSON responses and embeds them into your vector store. Now your team can ask their chat interface which pipelines feed the Snowflake instance, and the agent answers using live, indexed metadata.

Semantic search for data models

Finding specific transformation logic across a massive Hevo account is painful. You can use LlamaIndex to execute `list_models` and `list_workflows` on a schedule. The tool output gets chunked and indexed. When an analyst asks how the customer table gets cleaned, the agent searches the vector store, retrieves the exact model configuration, and explains the logic without hallucinating.

Ground your usage audits in real data

When finance asks why the data bill went up, you need hard numbers. Your LlamaIndex application calls `get_usage` and `get_pipeline` to pull the active event counts. Because the framework indexes this output, you can query historical usage trends over time. The agent cross-references the current API read with past indexed sessions to tell you exactly when the spike started.

Setup guide

Set up Hevo Data (ETL & Data Pipeline) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Hevo Data (ETL & Data Pipeline) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Hevo Data (ETL & Data Pipeline) tools.",
)
response = await agent.run("List recent Hevo Data (ETL & Data Pipeline) data")

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Common questions about Hevo Data (ETL & Data Pipeline) MCP in LlamaIndex

Install llama-index-tools-mcp and instantiate a BasicMCPClient. Wrap it with McpToolSpec and call to_tool_list_async() to feed the functions to your FunctionAgent.

Yes. If you periodically run the get_usage tool and index the results into your vector store, your agent can perform semantic queries against that historical data.

Dashboards require manual review. By indexing tools like list_workflows, you can ask natural language questions about your pipeline dependencies and get immediate, grounded answers.

Absolutely. You can pass an allowed_tools filter to the spec. If you only want the agent indexing destinations, restrict it to the list_destinations tool.

No. The tools strictly read infrastructure metadata, like pipeline IDs, destination names, and usage metrics. The integration runs inside an ephemeral sandbox that requires a single endpoint token, ensuring your underlying data rows remain entirely inaccessible.

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