QuestDB (Time-Series) Connector for AI agents.
4 live capabilities
Query and manage time-series data using natural language.
Waiting for input…
Why people use QuestDB (Time-Series)
QuestDB Time-Series for High-Speed SQL Analysis
With this Connector, you just stay in your chat. You tell your agent what you want to see, and it handles the SQL for you. You get the data you need without ever leaving your workspace.
What Vinkius changes
You get a conversational interface for your time-series data.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Analyzing sensor data
A technician asks the agent for the average temperature from the sensors table over the last hour to identify a spike.
- Real-world use case 02
Quick data exports
An analyst needs to grab the last 1000 trade records as a CSV for a meeting and asks the agent to export them.
- Real-world use case 03
Automated ingestion
A data engineer tells the agent to import a new TSV file into the database, letting the Connector handle the table creation.
Complete set · 4capabilities
The complete QuestDB (Time-Series) capability set.
These are the exact actions your AI can choose when you ask it to work with QuestDB (Time-Series).
01—04
4 capabilities in this set.
Part of 4 available through QuestDB (Time-Series).
- 01 Capability
Execute sql
Run standard SELECT, INSERT, or DDL statements on your QuestDB instance. This is perfect for quick data lookups or schema updates.
- 02 Capability
Export data
Extract large datasets from your database into CSV or Parquet formats. Use this to move data into your reporting capabilities.
- 03 Capability
Import data
Import tabular CSV or TSV data into QuestDB with automatic table creation. It handles the schema so you don't have to.
- 04 Capability
Ping
Check the current status and version of your QuestDB instance. Use this for quick health checks and connectivity tests.
Set up in minutes
One URL. Then ask QuestDB (Time-Series) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use QuestDB (Time-Series) from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_2JOk1hTa0Hc7TZSrk9PX0JHkHMR9yGZEnj7dIpoE/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it QuestDB (Time-Series), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable QuestDB (Time-Series) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_2JOk1hTa0Hc7TZSrk9PX0JHkHMR9yGZEnj7dIpoE/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the QuestDB (Time-Series) URL.
- Step 03
Save and start
Save the connection and enable QuestDB (Time-Series) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"questdb-time-series": {
"url": "https://edge.vinkius.com/vk_preview_2JOk1hTa0Hc7TZSrk9PX0JHkHMR9yGZEnj7dIpoE/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using QuestDB (Time-Series)
Open Agent mode in chat and ask: "Using QuestDB (Time-Series), help me...". 4 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"questdb-time-series": {
"url": "https://edge.vinkius.com/vk_preview_2JOk1hTa0Hc7TZSrk9PX0JHkHMR9yGZEnj7dIpoE/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using QuestDB (Time-Series)
Ask Copilot: "Using QuestDB (Time-Series), help me...". 4 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"questdb-time-series": {
"url": "https://edge.vinkius.com/vk_preview_2JOk1hTa0Hc7TZSrk9PX0JHkHMR9yGZEnj7dIpoE/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using QuestDB (Time-Series)
Open Cascade and ask: "Using QuestDB (Time-Series), help me...". 4 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"questdb-time-series": {
"url": "https://edge.vinkius.com/vk_preview_2JOk1hTa0Hc7TZSrk9PX0JHkHMR9yGZEnj7dIpoE/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using QuestDB (Time-Series)
Ask Cline: "Using QuestDB (Time-Series), help me...". 4 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add questdb-time-series --transport http "https://edge.vinkius.com/vk_preview_2JOk1hTa0Hc7TZSrk9PX0JHkHMR9yGZEnj7dIpoE/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using QuestDB (Time-Series)
Ask Claude: "Using QuestDB (Time-Series), show me...". 4 tools are ready
Where the request belongs
Work QuestDB can move forward.
This is for the data engineer who's tired of manual migrations, the analyst who hates writing complex SQL for every single report, and the DevOps person who needs to check database status without opening a new terminal.
Data Engineer
Manages migrations and verifies ingestion pipelines by asking the agent to inspect schemas and run DDL.
Data Analyst
Performs ad-hoc time-series analysis and exports results for presentations without writing manual scripts.
DevOps Engineer
Monitors database health and performs routine maintenance through a conversational interface.
Build the capability set
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Bring your own AI
Change the model, client or framework. Keep QuestDB connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about QuestDB.
The practical details behind the request, access and result.
What can I do with the QuestDB Time-Series MCP?
You can use natural language to run SQL queries, import data files like CSVs, and export results for your reports.
How does the QuestDB Time-Series MCP handle my data?
It connects directly to your instance, allowing your AI client to read, write, and export time-series data on your behalf.
Can the QuestDB Time-Series MCP import files for me?
Yes, it can automatically create the necessary tables and columns when you import tabular data like CSV or TSV files.
How do I check my QuestDB instance status?
You can simply ask your AI agent to check the status, and it will ping the instance to confirm it is online and show you the version.
Can I export data from QuestDB using this?
Yes, the Connector can extract your query results into CSV or Parquet formats, making it easy to move data into other capabilities.
Is the QuestDB Time-Series MCP good for IoT data?
It's ideal for IoT data because it's designed specifically for high-performance time-series analysis and SQL queries.
Can I execute standard SQL queries and DDL commands like creating tables?
Yes! Use the execute_sql capability to run any valid QuestDB SQL statement, including SELECT, INSERT, and table definitions. You can also include parameters like explain to see the execution plan.
How do I import a CSV file into a new or existing table?
Use the import_data capability. Provide the target table name and the raw CSV data. The capability can automatically create the table structure and handle partitioning if specified.
Is there a way to export large query results for use in other capabilities?
Absolutely. The export_data capability allows you to run a query and receive the output in CSV or Parquet format, which is ideal for large-scale data extraction.
One connection away
Give your agent a direct line to QuestDB.
Connect QuestDB once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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