# QuestDB Time-Series MCP for AI Agents AI Agent Connect > QuestDB (Time-Series) MCP gives your AI agent direct access to your time-series database. You can run SQL queries, ingest CSV or TSV files, export data to Parquet or CSV, and check the database status using natural language. It's built for high-speed data management without the friction of manual SQL writing. Whether you are an analyst needing a quick report or a data engineer managing pipelines, this Connector handles the heavy lifting of query syntax and data movement. ## Overview - **Category:** databases - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_2JOk1hTa0Hc7TZSrk9PX0JHkHMR9yGZEnj7dIpoE/ai-agent-connect - **Tags:** time-series, sql, data-ingestion, real-time-analytics, questdb ## Description Managing time-series data usually feels like a chore of constant context switching. You have to jump between your analysis tool and your database console, manually typing out SQL queries just to see what's happening with your sensors, stock prices, or trade logs. It's a constant context switch that breaks your focus and slows down your progress. This Connector changes that by letting your AI agent do the heavy lifting. Instead of hunting for the right syntax or worrying about complex joins, you just tell your agent what you're looking for in plain English. It handles the connection to your QuestDB instance and executes the commands for you. Whether you're trying to pull a specific slice of data for a morning report, checking the health of your database, or needing to bulk-import a new dataset from a CSV file, you can just ask for it. It's a lot easier to stay in your flow when you don't have to worry about the underlying query structure or the technicalities of partitioning. If you're looking for a way to manage your database without leaving your primary workspace, this is how you do it. It's one of the many ways Vinkius helps you bridge the gap between your data and your AI tools, making it easier to turn raw numbers into actual insights. ## Tools ### execute_sql Run standard SELECT, INSERT, or DDL statements on your QuestDB instance. This is perfect for quick data lookups or schema updates. ### export_data Extract large datasets from your database into CSV or Parquet formats. Use this to move data into your reporting tools. ### import_data Import tabular CSV or TSV data into QuestDB with automatic table creation. It handles the schema so you don't have to. ### ping Check the current status and version of your QuestDB instance. Use this for quick health checks and connectivity tests. ## Prompt Examples **Prompt:** ``` Is the database online? ``` **Response:** ``` I've checked the status. Your QuestDB instance is currently online and running version 8.1.0. ``` **Prompt:** ``` What was the average temperature from the sensors table in the last hour? ``` **Response:** ``` The average temperature for the last hour is **22.5°C**. This was calculated from **1,200 data points** in your sensors table. ``` **Prompt:** ``` Can you get the last 1000 trades out as a CSV? ``` **Response:** ``` I've started that export for you. You can now download the CSV containing the last 1000 trade records. ``` ## Capabilities ### Run SQL queries Execute SELECT, INSERT, or DDL statements on your QuestDB instance using natural language. ### Bulk import data Bring in CSV or TSV files while the Connector handles table and column creation automatically. ### Export datasets Get your query results out in CSV or Parquet formats for use in other applications. ### Check database health Get instant status updates and version info for your QuestDB setup. ### Manage schemas Update your database structure and run migrations without memorizing complex syntax. ## Use Cases ### Analyzing sensor data A technician asks the agent for the average temperature from the sensors table over the last hour to identify a spike. ### 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. ### Automated ingestion A data engineer tells the agent to import a new TSV file into the database, letting the Connector handle the table creation. ### Database maintenance A DevOps lead asks the agent to check the current QuestDB version and status to ensure the instance is healthy. ## Benefits - Stop writing manual SQL: Use execute_sql to run queries by just describing what data you need. - Faster data ingestion: Use import_data to bring in CSVs without worrying about manual table creation. - Easy data portability: Use export_data to quickly grab results in Parquet or CSV for other apps. - Instant health checks: Use ping to verify your database is online and check the version in seconds. - Natural language migrations: Update your schemas and DDL structures without memorizing complex syntax. ## How It Works The bottom line is you get a conversational interface for your time-series data. 1. Subscribe to the Connector and provide your QuestDB URL and credentials. 2. Connect your AI client to the Vinkius catalog. 3. Ask your agent to run queries, import files, or export data. ## Frequently Asked Questions **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 tools. **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` tool 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` tool. Provide the target table name and the raw CSV data. The tool can automatically create the table structure and handle partitioning if specified. **Is there a way to export large query results for use in other tools?** Absolutely. The `export_data` tool allows you to run a query and receive the output in CSV or Parquet format, which is ideal for large-scale data extraction.