Starburst MCP. Query federated data lakes with natural conversation.
Starburst MCP connects your AI client directly to enterprise federated data lakes. It lets you run complex SQL queries against diverse sources like Snowflake and S3, check schemas, and manage access roles—all using natural conversation. You query massive, distributed datasets without ever leaving your chat window or needing multiple database connection tools.
Give Claude and any AI agent real-world access
You execute advanced SQL commands against massive data sources and receive structured results directly.
The system lists all the major data catalogs attached to your network, showing you where your data lives.
You drill down into specific databases to see exactly what schemas and tables are available for querying.
The MCP lists all the pre-approved, structured data products ready for analysis across your enterprise.
You verify who has access to what by listing security roles and checking current assignments.
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What AI agents can do with Starburst MCP with 6 Tools
Use these tools to manage the structure of your data environment, from listing all connected catalogs to running specific SQL queries.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Starburst MCPGet Query Details
Retrieves specific information about a particular SQL query you ran.
List Catalogs
Lists all the main data catalogs available across your entire Starburst network.
List Data Products
Lists every published, pre-packaged analytical dataset ready for consumption.
List Domains
Shows the various domains that organize your data products.
List Queries
Retrieves a history of recent SQL queries executed in the system cluster.
List Roles
Displays all security roles defined across your organization's data environment.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Starburst, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Starburst. 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.
VINKIUS CLOUD
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Data auditing and querying used to feel like an endless series of clicks.
Today, getting a full view requires logging into the data warehouse UI, running a query, downloading the CSV, then switching to the governance portal to check permissions, and finally opening the schema documentation just to figure out what column names mean. It's constant context switching.
With this MCP, you tell your agent exactly what data you need and from where. The AI client does all that cross-referencing—listing catalogs, checking schemas, running the SQL—and hands you a single answer.
Starburst MCP gives you immediate control over your data environment.
You no longer have to manually run `list_catalogs` and then drill down through multiple screens just to see what databases are even connected. The system maps it out for you automatically when you ask a general question.
It means your data exploration moves at the speed of thought, not the speed of clicking through a dozen different administrative dashboards.
What Starburst MCP does for your AI
This MCP brings the power of enterprise data analytics into your conversational AI workflow. Instead of opening ten different dashboard tabs or writing boilerplate SQL connection scripts for every source, you talk to your agent and ask questions about your combined data lakes. Your client uses this MCP to figure out which sources are connected (like S3 or Snowflake) and lets you query them as if they were one giant database.
You can run complex queries against the entire system, check what schemas exist across different departments, and even verify who has access to sensitive information. It’s about making data governance and advanced querying feel natural. When working with other enterprise tools, Vinkius makes sure this MCP is available alongside thousands of others, so your AI client never gets stuck needing a new connection.
019d760d-3e66-71e5-8f6a-015ebd0e9756 How to set up Starburst MCP
The bottom line is that this MCP turns complex, multi-step database interactions into simple conversation prompts.
First, you install the Starburst MCP connector, linking it securely to your active AI client.
Next, in the MCP settings, you provide your STARBURST_HOST and STARBURST_TOKEN to establish a persistent connection session.
Finally, you just ask your agent: 'Show me the top 10 rows from customer analytics.' The MCP handles the rest.
Who uses Starburst MCP
This is for data professionals who spend too much time wrestling with connection strings and switching between dozens of specialized dashboard tools. It's the analyst who needs to check three different systems just to get a complete picture, or the engineer who dreads writing complex boilerplate code every single week.
They use this MCP to explore massive federated datasets by simply asking for reports and executing complex SQL without building connection scripts.
They rely on it to parse schemas, manage catalogs, and iterate over queries conversationally instead of writing detailed auditing code.
They use this MCP to maintain oversight by verifying role assignments and checking internal data products without logging into multiple security consoles.
Benefits of connecting Starburst MCP
You get immediate visibility into your entire data landscape. Instead of manually checking multiple systems, running list_catalogs shows all connected sources in one go.
Complex reporting becomes simple talking. You write a prompt like 'top 10 customer records' and the agent executes it instantly using execute_query, getting structured results back.
Data governance is simplified. Need to know who can see payroll data? Use list_roles to review security assignments without logging into an admin portal.
Never get lost in your schema again. You can use list_schemas to drill down and map out exactly what tables exist inside a specific database structure.
Discover approved datasets easily. Instead of guessing which dataset is correct, run list_data_products to see every published data product ready for analysis.
Starburst MCP use cases
Finding the source of truth for sales metrics
A Data Analyst needs to compare sales figures from the production system (Snowflake) against archived records (S3). Instead of writing a massive script with three connection points, they ask their agent. The MCP uses list_catalogs and then runs an execute_query across both sources, giving them one unified result set.
Auditing data access for compliance
A Governance Manager needs to prove that only the Finance team can view salary data. They prompt the agent to run list_roles, verifying that the 'analyst' role lacks permission, and then cross-reference this with active assignments.
Quickly diagnosing a broken report
A Data Engineer notices a dashboard is failing. They ask their agent to run list_queries to check recent failures, or use get_query_details to see exactly what parameters caused the failure in the last run.
Preparing for a new feature launch
The team needs a dataset combining marketing and sales data. They first use list_data_products to identify the existing components, then ask the agent to construct an execute_query that links them together.
Starburst MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Connecting system by system
Writing separate connection blocks for Snowflake and S3 just because you need data from both. This takes hours of boilerplate code.
Tell your agent to query the combined dataset. The MCP handles multiple sources automatically, so all you do is ask one conversational question using execute_query.
Forgetting current permissions
Writing a powerful query and running it only to find out later that your account doesn't have access to the required table.
Before writing code, always run list_roles and check the security assignments. This confirms you have the necessary access before wasting time on failed queries.
Not knowing what data exists
Opening a database console only to find dozens of schemas, none of which are clearly labeled or documented.
Start by running list_catalogs and then use list_schemas to systematically map out the available databases before you start building queries.
When to use Starburst MCP
Use this MCP if your core problem is querying data spread across multiple, diverse sources—like combining logs from S3 with structured data in Snowflake. You need natural language control over complex SQL and robust data governance checks using tools like list_roles or list_data_products. Don't use this if you simply need to send a message or manage user contacts; those are messaging MCPs. Also, don't use it if your only goal is simple document retrieval; for that, look at a dedicated knowledge base tool instead.
Frequently asked questions about Starburst MCP
How does Starburst MCP handle multiple database types? +
The MCP is designed to query federated data lakes. It connects to diverse sources like Snowflake and S3, allowing you to run a single query against all of them.
Can I see which roles exist using Starburst MCP? +
Yes, running list_roles allows the agent to display every security role defined in your organization's data environment for auditing purposes.
What is the difference between list_catalogs and list_schemas? +
Using list_catalogs shows the highest level of grouping (the entire database instance), while list_schemas lets you drill down to see the specific groups of tables within one catalog.
Does Starburst MCP help with data discovery? +
Absolutely. By listing available data products using list_data_products, it helps you find pre-approved, curated datasets without knowing their exact location or schema name.
What is the best way to check query history with Starburst MCP? +
You use the list_queries tool. This lets your agent retrieve a clean record of recent SQL queries executed in the cluster for review.