Can the AI run write or drop queries?

That depends on your token's permissions. The `execute_query` tool passes any SQL to Starburst. To prevent mutations, provision a read-only token via your Starburst security settings.

How do I handle queries that return thousands of rows?

Always add a `LIMIT` clause in your SQL. Large result sets can exceed the AI's context window. Instruct the agent to use `LIMIT 10` or `LIMIT 50` to keep responses manageable.

What credentials do I need?

Two values: `STARBURST_HOST` (your cluster URL, e.g., `https://mycluster.trino.galaxy.starburst.io`) and `STARBURST_TOKEN` (a service account token from your Starburst admin settings).

Starburst MCP Server for LlamaIndex 6 tools — connect in under 2 minutes

Name: Starburst
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Starburst as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Starburst. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Starburst?"
    )
    print(response)

asyncio.run(main())

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* 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 Starburst MCP Server

Integrate the powerful federated data analytics capabilities of Starburst directly into your conversational AI workflows. Empower your data engineering and analytics teams to query extensive data lakes, manage organizational roles, and explore detailed schemas without needing to explicitly switch between database clients. Securely map your AI assistant to your Starburst host, enabling natural language orchestration of complex Trino-based data products to accelerate data discovery and governance across your entire enterprise architecture.

LlamaIndex agents combine Starburst tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the 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

Federated Query Execution — Pass complex SQL statements programmatically against your connected data sources utilizing execute_query, receiving structured analytic returns directly.
Schema & Catalog Discovery — Actively map your data landscape by inspecting linked databases invoking list_catalogs, and drill down into specific table hierarchies using list_schemas.
Data Product Management — Manage and retrieve existing analytical data products across the Starburst network systematically validating data definitions using list_data_products.
Governance & Role Administration — Inspect access control limitations securely by navigating role assignments formally deploying requests through list_roles and evaluating privilege thresholds.

The Starburst 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 Starburst to LlamaIndex via MCP

Follow these steps to integrate the Starburst 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 Starburst

Why Use LlamaIndex with the Starburst MCP Server

LlamaIndex provides unique advantages when paired with Starburst through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Starburst tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Starburst tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Starburst, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Starburst tools were called, what data was returned, and how it influenced the final answer

Starburst + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Starburst MCP Server delivers measurable value.

Hybrid search: combine Starburst real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Starburst 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 Starburst for fresh data

Analytical workflows: chain Starburst queries with LlamaIndex's data connectors to build multi-source analytical reports

Starburst MCP Tools for LlamaIndex (6)

These 6 tools become available when you connect Starburst to LlamaIndex via MCP:

get_query_details

Retrieves details for a specific SQL query

list_catalogs

g., S3, Snowflake, Iceberg) are connected. Lists all data catalogs available in Starburst Galaxy

list_data_products

Lists all published data products

list_domains

Lists data product domains

list_queries

Lists recent SQL queries executed in the cluster

list_roles

Lists all security roles in the organization

Example Prompts for Starburst in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Starburst immediately.

"List all active operational catalogs across the current data lake instance, and fetch the underlying schematics of any source containing the designation 'finance' in its structure."

"Execute a query to retrieve the top 10 rows from the 'customer_analytics' table located in our 'production_hive' catalog."

"List all registered data products across the Starburst network and check current role assignments to ensure proper access."

Troubleshooting Starburst MCP Server with LlamaIndex

Common issues when connecting Starburst to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Starburst + LlamaIndex FAQ

Common questions about integrating Starburst MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Starburst tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.