SingleStore MCP. Query live data and manage your entire database infrastructure.
SingleStore MCP gives your AI agent direct, read-and-write access to your SingleStore data infrastructure. Run raw SQL queries, execute semantic vector searches, list all workspaces, and audit billing usage—all from your preferred chat interface.
Give Claude and any AI agent real-world access
The agent executes raw SELECT statements against a specified database to retrieve precise data points.
It runs semantic vector searches, finding the closest matches within your dataset based on mathematical proximity.
The agent can list all existing SingleStore workspaces and organizations associated with your account.
It lists all specific databases located within a given workspace ID so you know where to run queries.
You retrieve real-time metrics on your account's resource consumption and associated costs using get_billing_usage.
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What AI agents can do with SingleStore MCP: 6 Tools for Database Control
These tools give your agent the power to interact with every part of your SingleStore environment, from running queries to checking resource usage.
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 SingleStore MCPExecute Sql
Sends any standard SQL query to the database for execution, retrieving the resulting dataset.
Get Billing Usage
Retrieves a summary report showing current resource usage and billing metrics for...
List Databases
Generates a list of all individual databases that exist within a specified workspace...
List Organizations
Provides a comprehensive catalog of all organizations currently linked to the user's...
List Workspaces
Lists every available workspace configured under the SingleStore infrastructure.
Vector Search
Executes a similarity search to find records whose data vectors are closest to a provided query vector.
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 SingleStore, 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 SingleStore. 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
The headache of context switching in database management
Right now, checking your data environment is a manual pain. You open Dashboard A to see what workspaces exist. Then you jump to the Admin Console B to check billing limits. If you want to run a query, you have to go to Editor C, making sure you selected the right database and that your credentials are correct for that specific task.
With this MCP, all those separate hops disappear. You talk to your agent once, telling it exactly what context you need—say, 'Show me the user count and the current monthly cost.' The agent handles querying `list_workspaces`, running an `execute_sql` query, and calling `get_billing_usage` automatically. You just get the answer.
SingleStore MCP: Direct Data Access
You no longer have to copy IDs from one tab, paste them into a second script, and then manually switch to a third dashboard just to validate the scope. The agent manages these dependencies behind the scenes.
What's different is control. You don't just get *an* answer; you get an executed result derived directly from your live SingleStore infrastructure, giving you full read-and-write sovereignty over your data.
What SingleStore MCP does for your AI
This MCP lets your AI client act like a full database administrator for your SingleStore setup. You stop bouncing between external dashboards just to check schema details or run complex search joins. Instead, you talk to your agent, which then executes the necessary commands against your live data. It handles everything from running raw SQL queries on demand to performing advanced vector similarity searches using vector_search.
Plus, it keeps an eye on costs, letting you audit billing usage with a simple request for metrics. When integrated via Vinkius, this single connection gives your agent total control over the entire SingleStore environment, allowing deep data analysis without ever leaving your workflow.
019d7608-6b99-7129-8c92-8d643dc7228c How to set up SingleStore MCP
The bottom line is that your AI client handles the entire round trip: understanding the need, running the query against the live database, and presenting the clean output.
Tell your AI client what data you need (e.g., 'Show me all users who logged in last month').
The MCP analyzes the request, identifies the necessary tools, and executes the required actions, such as running execute_sql or calling vector_search.
Your agent receives structured results—the raw data tables, lists of workspaces, or usage metrics—and presents them directly to you.
Who uses SingleStore MCP
Data Engineers who hate manually scripting connectivity checks; ML Scientists needing immediate access to raw vectors for model training; or Database Administrators tired of switching between monitoring dashboards.
Uses the MCP to programmatically list available databases and workspaces, ensuring their ETL pipelines always point to the correct endpoints.
Feeds unstructured text into the system and uses vector_search to find related records in the database for model fine-tuning or research.
Runs administrative checks by using list_workspaces and auditing costs via get_billing_usage without logging into a separate console.
Benefits of connecting SingleStore MCP
Stop switching tabs. You run complex queries using execute_sql directly through conversation, getting immediate results instead of waiting for GUI forms to load.
Deep dive into unstructured data. Instead of manual keyword searches, use vector_search to find meaningful connections between records based on semantic similarity.
Know your limits before they bite you. Use the dedicated billing tools like get_billing_usage to audit costs and usage patterns instantly.
Manage infrastructure from one place. The agent handles listing everything—from all workspaces via list_workspaces to specific databases with list_databases—without needing admin console access.
Accelerate development cycles. You can list organizations or run simple checks like list_users, providing context to your AI client before writing complex code.
SingleStore MCP use cases
Finding the right data source for a new project
A Data Engineer needs to know if their team has already set up an environment. They ask their agent, which immediately runs list_workspaces and presents a clean list of available IDs, saving them manual browsing.
Investigating a sudden spike in cloud costs
A DBA suspects resource overage. Instead of pulling up the billing dashboard, they ask their agent to run get_billing_usage, getting an immediate, actionable metric report back in seconds.
Connecting text search to structured data
An ML Scientist has a block of unstructured user feedback. They feed it into the agent and use vector_search to pull up the 10 most semantically related customer records from the database, instantly grounding their research.
Verifying data availability before coding
A developer needs to know if a specific module exists. They ask the agent to list_databases within a known workspace ID, verifying that the schema is ready for development without running any code.
SingleStore MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Manually scripting resource checks
A user writes Python code that must connect to three separate APIs—one for workspaces, one for billing, and a third for the database schema just to get basic context.
Let your AI client use this MCP. It coordinates calling list_workspaces, then running get_billing_usage, all in response to a single natural language prompt.
Running complex queries without scope
Writing an SQL query that references tables or databases that don't actually exist because the user forgot which workspace they were working in.
First, ask your agent to run list_workspaces and confirm the correct environment. Then execute the query using execute_sql.
Treating vector search like a keyword lookup
Running a simple text search that only matches exact words, missing out on contextually similar results because it ignores semantic relationships.
Use the vector_search tool. It understands meaning and finds records based on similarity vectors, giving you much richer insights than basic keyword matching.
When to use SingleStore MCP
Use this MCP if your primary need is querying or auditing data already within SingleStore's ecosystem. You need the agent to run raw commands like execute_sql or perform specialized searches like vector_search. Don't use it if you are trying to build a whole new database from scratch; for that, you need an ETL pipeline tool. Also, don't rely on this MCP for user interface actions (like clicking 'Save' or 'Submit'); its job is purely data retrieval and administration. If your goal is only basic reporting without complex joins, standard BI tools might suffice, but if the query logic needs to be dynamic and based on current system state, use this MCP.
Frequently asked questions about SingleStore MCP
How do I use the singlestore MCP to check my monthly bill? +
You ask the agent to audit billing usage. It calls get_billing_usage and returns a report on your current resource consumption and associated costs.
Is singlestore MCP for semantic search or just SQL queries? +
It handles both. While you can run raw SQL with execute_sql, the dedicated vector_search tool lets you perform advanced, meaning-based similarity searches against your data.
What if I need to know which databases are available? +
You first use list_workspaces to find the container ID. Then, tell the agent to run list_databases using that specific workspace ID to see all contained schemas.
Can I list organizations with singlestore MCP? +
Yes, simply request a list of associated accounts. The agent uses list_organizations to pull up a catalog of all linked organizational entities for your account.
Do I need to run raw SQL queries every time with singlestore MCP? +
No. While execute_sql is powerful, you can also use the agent for administrative tasks like checking resource usage via get_billing_usage, which doesn't require running a query.