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 LangChain 6 tools — connect in under 2 minutes

Name: Starburst
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Starburst through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "starburst": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Starburst, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Starburst through native MCP adapters. Connect 6 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Starburst MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 6 tools from Starburst via MCP

Why Use LangChain with the Starburst MCP Server

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

The largest ecosystem of integrations, chains, and agents — combine Starburst MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Starburst queries for multi-turn workflows

Starburst + LangChain Use Cases

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

RAG with live data: combine Starburst tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Starburst, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Starburst tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Starburst tool call, measure latency, and optimize your agent's performance

Starburst MCP Tools for LangChain (6)

These 6 tools become available when you connect Starburst to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Starburst + LangChain FAQ

Common questions about integrating Starburst MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.