QuestDB (Time-Series) MCP. Querying metrics, logs, and trends via natural language.
QuestDB connects your AI agent directly to a high-performance time-series database, letting you run complex data queries using natural language. It handles everything from real-time metrics analysis and bulk data ingestion to exporting massive datasets in CSV or Parquet format.
Give Claude and any AI agent real-world access
The agent executes standard SELECT, INSERT, and DDL statements to query or modify data in the QuestDB instance.
You can feed tabular files like CSV or TSV directly into tables; the MCP automatically figures out which columns are needed.
It pulls query results and exports them immediately as ready-to-use CSV or Parquet files for external analysis.
The agent runs a quick check to confirm the server is online and reports its current version number.
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What AI agents can do with QuestDB (Time-Series) - 4 Tools
These tools allow your agent to run SQL queries, import raw files, check the server health, and export results from the QuestDB time-series database.
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 QuestDB (Time-Series) MCPExecute Sql
Use this to run any standard SQL operation, like querying specific metrics or making schema changes (DDL/DML).
Export Data
Extracts the results from a query and packages them for easy download as CSV or...
Import Data
Feeds in new data from CSV or TSV, automatically setting up the necessary tables and...
Ping
Confirms the database server is operational and returns its current version number.
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 QuestDB (Time-Series), 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 QuestDB. 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.
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The Pain of Manual Data Extraction
Today, getting a clear picture of system performance means jumping between dashboards, writing complex SQL queries that take hours to debug, and then manually running exports just so you can attach the data to a slide deck. It's copy-pasting numbers from one tab into another, hoping you didn't miss a time zone or a crucial metric.
With this MCP connected via Vinkius, your agent handles all that complexity. You simply tell it what metrics you need and what date range you care about. The agent executes the query, pulls the specific data points, and delivers a clean, ready-to-use export file—no manual clicking required.
QuestDB: Instant Data Access
The ability to run ad-hoc queries is immediate. You don't need a dedicated SQL window; you just ask for the average temperature, and the agent uses `execute_sql` to get it. Need to check if the service is up? One prompt runs the `ping` tool.
What changes is that your workflow moves from 'write-debug-run-export' to simply 'ask.' You get real-time insights directly in your conversational interface.
What QuestDB (Time-Series) MCP does for your AI
You can treat your database like an extension of your conversation. Instead of writing boilerplate SQL, simply ask your agent for the average temperature over the last hour or check what happened to a metric two weeks ago. This MCP lets you run complex queries and manage time-series data—whether it's sensor readings, stock prices, or server logs—all through natural language commands.
You can also import large amounts of raw data, which automatically builds the necessary tables and schema for you. If your agent needs to extract results for a report, it exports everything cleanly as CSV or Parquet files. Vinkius hosts this MCP, making high-speed time-series analysis available from any compatible client.
019e38de-cb28-732b-bc30-ed8e4637e2b6 How to set up QuestDB (Time-Series) MCP
The bottom line is you get high-speed access to complex time-series functions without ever leaving your natural language interface.
First, subscribe to this MCP on Vinkius and provide your specific QuestDB connection URL along with any necessary authentication credentials.
Next, direct your agent to perform a task—for instance, asking it to find the average CPU utilization for last month or import a new batch of sensor readings.
The system executes the required operation against the database and returns the actionable result, whether that's a data table, an exported file link, or a simple status message.
Who uses QuestDB (Time-Series) MCP
Anyone who deals with metrics, logs, or data that changes over time needs this. This MCP is essential for the DevOps engineer tired of clicking through dashboard panels at 2 a.m., and the analyst who can't wait for a DBA to write a custom SQL query.
Performing ad-hoc analysis on historical metrics, such as calculating year-over-year growth or finding the average value of sensor readings over specific time windows.
Monitoring database health and running maintenance tasks. They use this to check server status or verify that data ingestion pipelines are running correctly.
Managing the lifecycle of time-series datasets, including creating new tables, modifying schemas, and importing large batches of raw telemetry data.
Benefits of connecting QuestDB (Time-Series) MCP
Stop writing boilerplate SQL. Instead of crafting a complex query for every metric you need, just ask your agent to run the SELECT statement using execute_sql. You get the data instantly.
Handling massive datasets is easy. Use import_data to drop CSV or TSV files into the database. The MCP handles schema creation and partitioning automatically, so you don't have to pre-process anything.
Reporting shouldn't require manual exports. After running a query with execute_sql, use export_data to get your results as clean CSV or Parquet files ready for sharing.
Know if the database is healthy before you start. Running the ping tool gives you instant server status and version confirmation, letting you verify operational status in seconds.
The whole process feels seamless. You keep everything within your chat interface—from querying data to exporting it—without ever opening a separate SQL client.
QuestDB (Time-Series) MCP use cases
Finding the average server load for last quarter
A DevOps engineer needs to know the quarterly trend of CPU usage. They ask their agent, which uses execute_sql to run a time-series query and returns the specific average metric, eliminating the need to dive into multiple dashboard tabs.
Onboarding new sensor data streams
An analyst receives a large batch of raw temperature readings in a CSV. Instead of manually writing schema creation scripts, they use import_data and simply upload the file; the MCP builds the table structure automatically.
Preparing data for an external report
A product manager asks their agent to pull the top 10 most active users from a specific period. The agent uses execute_sql and then calls export_data, giving the PM a clean CSV file they can immediately attach to a presentation.
Checking database connectivity during an incident
When the application goes down, a support technician first runs the ping tool. The agent confirms the server status and version instantly, isolating whether the problem is connectivity or a logic bug.
QuestDB (Time-Series) MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Treating it like a document search
Trying to ask QuestDB for 'all documents mentioning latency spikes'. The database only handles structured time-series metrics, not unstructured text.
If you have time-stamped metrics (like 'latency_ms'), use execute_sql with a specific column name and time range. Use the MCP to query data that has a clear structure.
Writing massive, multi-step scripts
The user writes a 50-line script: connect -> select -> process -> export.
Use the agent's natural language interface. Ask it to 'find X and export as CSV'. The MCP handles calling execute_sql followed by export_data in two simple steps.
Ignoring data source validation
Trying to manually write the schema for a new dataset, risking missing columns or incorrect types.
Use import_data. The tool automatically inspects your uploaded CSV/TSV file and creates the table structure for you, saving hours of manual DDL work.
When to use QuestDB (Time-Series) MCP
You should use this MCP if your data is fundamentally time-based: logs, sensor readings, financial metrics, or performance counters. If you need to know 'what happened at X time,' this is the tool. Don't use it if your primary goal is unstructured text search (like searching a knowledge base) or general relational joins across disparate systems that aren't time-indexed. For pure document storage and retrieval, look for vector database MCPs instead.
Frequently asked questions about QuestDB (Time-Series) MCP
How do I connect QuestDB (Time-Series) MCP using the `ping` tool? +
You just ask your agent to check the status. The agent automatically runs the ping function, which confirms if the database is online and reports its current version number for you.
Can QuestDB (Time-Series) MCP handle data I don't have a schema for? +
Yes. Use the import_data tool. You upload your CSV or TSV file, and the MCP automatically detects and creates the necessary tables and columns before ingestion.
What is the best way to get data out of QuestDB (Time-Series) MCP? +
For reporting, use export_data. It takes your query results and packages them into professional CSV or Parquet files that are ready for any external analysis tool.
Does the `execute_sql` tool support complex joins? +
Yes. Since it executes standard SQL, you can run full DML/DDL operations and perform complex joins across different tables within your time-series data.
Is QuestDB (Time-Series) MCP only for monitoring logs? +
No. While great for logs, it handles any metric that changes over time—think stock prices, sensor readings, or server usage counts—as long as the data is structured by time.