Hevo Data (ETL & Data Pipeline) MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Hevo Data (ETL & Data Pipeline) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Hevo Data (ETL & Data Pipeline). "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Hevo Data (ETL & Data Pipeline)?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Hevo Data (ETL & Data Pipeline) MCP Server
Connect your Hevo Data account to any AI agent and take full control of your automated data integration and ETL orchestration through natural conversation.
LlamaIndex agents combine Hevo Data (ETL & Data Pipeline) tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Pipeline Orchestration — List all running ETL pipelines and extract explicit routing mappings linking ingestion frequencies to specific IDs directly from your agent
- Destination Monitoring — Analyze global warehouse targets (BigQuery, Snowflake, Redshift) terminating your replication runs and ensuring data delivery
- Transformation Models — Track explicitly attached mappings and transformations bounding your staging logic to maintain data quality
- Workflow Automation — Discover orchestration bounds and DAG workflows connecting transformations natively across your entire data stack
- Usage & Billing Audit — Access account usage metrics and billing ceilings to monitor row replications and overall account health in real-time
The Hevo Data (ETL & Data Pipeline) MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Hevo Data (ETL & Data Pipeline) to LlamaIndex via MCP
Follow these steps to integrate the Hevo Data (ETL & Data Pipeline) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from Hevo Data (ETL & Data Pipeline)
Why Use LlamaIndex with the Hevo Data (ETL & Data Pipeline) MCP Server
LlamaIndex provides unique advantages when paired with Hevo Data (ETL & Data Pipeline) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Hevo Data (ETL & Data Pipeline) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Hevo Data (ETL & Data Pipeline) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Hevo Data (ETL & Data Pipeline), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Hevo Data (ETL & Data Pipeline) tools were called, what data was returned, and how it influenced the final answer
Hevo Data (ETL & Data Pipeline) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Hevo Data (ETL & Data Pipeline) MCP Server delivers measurable value.
Hybrid search: combine Hevo Data (ETL & Data Pipeline) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Hevo Data (ETL & Data Pipeline) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Hevo Data (ETL & Data Pipeline) for fresh data
Analytical workflows: chain Hevo Data (ETL & Data Pipeline) queries with LlamaIndex's data connectors to build multi-source analytical reports
Hevo Data (ETL & Data Pipeline) MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Hevo Data (ETL & Data Pipeline) to LlamaIndex via MCP:
get_pipeline
Get pipeline details
get_usage
Get account usage
list_destinations
List all destinations
list_models
List all models
list_pipelines
List all pipelines
list_workflows
List all workflows
Example Prompts for Hevo Data (ETL & Data Pipeline) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Hevo Data (ETL & Data Pipeline) immediately.
"List all my active Hevo pipelines"
"Show me the destinations for my 'Sales Data' pipeline"
"How much of my row quota have I used this month?"
Troubleshooting Hevo Data (ETL & Data Pipeline) MCP Server with LlamaIndex
Common issues when connecting Hevo Data (ETL & Data Pipeline) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpHevo Data (ETL & Data Pipeline) + LlamaIndex FAQ
Common questions about integrating Hevo Data (ETL & Data Pipeline) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Hevo Data (ETL & Data Pipeline) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Hevo Data (ETL & Data Pipeline) to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.