Execute SQL queries and manage databases seamlessly with Timeplus. Leverage powerful tools to interact with your data, Kafka topics, and Iceberg tables efficiently. Enhance your data workflows with a user-friendly interface and robust backend capabilities.
https://github.com/jovezhong/mcp-timeplusStop switching between your AI assistant and your streaming data platform. This MCP server brings Timeplus's real-time analytics directly into Claude, letting you query streaming data, explore Kafka topics, and set up ETL pipelines without leaving your conversation.
You're already talking to Claude about your streaming architecture. Now you can actually interact with your real-time data while you're planning, debugging, or exploring. Query live streams, peek at Kafka messages, and analyze time-series data - all in the same conversation where you're discussing the implementation.
SQL Against Streaming Data: Execute queries directly on your Timeplus streams and tables. Get results from time-windowed aggregations, real-time joins, and continuous queries without opening another tool.
Kafka Topic Exploration: List topics, sample messages, and understand your data flow. Perfect for debugging pipeline issues or exploring new data sources during development.
Streaming ETL Setup: Create streaming ingestion pipelines from Kafka to Timeplus tables with a single command. Turn data exploration into immediate pipeline creation.
Apache Iceberg Integration: Query your data lake tables alongside streaming data for comprehensive analytics spanning real-time and historical data.
Pipeline Debugging: "Show me the last 10 messages from the user-events topic" followed by "Create a stream to save these to a local table for analysis."
Data Exploration: Query live metrics while discussing architecture changes. See actual data patterns inform your streaming window sizes and aggregation strategies.
Monitoring Setup: Build monitoring queries by exploring live data, then turn successful explorations into alerts or dashboards.
Schema Discovery: List tables and explore data types across your Timeplus workspace to understand your streaming data landscape.
Add this to your Claude Desktop config:
{
"mcpServers": {
"mcp-timeplus": {
"command": "uvx",
"args": ["mcp-timeplus"],
"env": {
"TIMEPLUS_HOST": "your-timeplus-host",
"TIMEPLUS_USER": "your-username",
"TIMEPLUS_PASSWORD": "your-password",
"TIMEPLUS_KAFKA_CONFIG": "{\"bootstrap.servers\":\"your-kafka:9092\"}"
}
}
}
}
Works with both Timeplus Cloud and on-premises deployments. The server includes safety defaults (read-only mode) that you can adjust based on your needs.
Unlike general database MCP servers, this one understands streaming data patterns. It includes specialized prompts that help Claude generate better SQL for time-series analysis, windowed operations, and real-time aggregations.
The tools are designed around streaming workflows - from exploring Kafka topics to setting up continuous ingestion, everything focuses on the real-time data pipeline use case.
Perfect for data engineers working with modern streaming stacks who want their AI assistant to understand their actual data, not just theoretical examples.