RisingWave MCP. Process, ingest, and query real-time data streams.

RisingWave gives your AI client direct hands-on control of your real-time streaming database. You don't need to jump between a SQL console, an API playground, or documentation just to run a query. This MCP Server puts the entire data lifecycle—from source connection to final sink output—right into natural conversation with your agent.

It lets you manage everything in one place.

When you use this server, your agent treats the database like a fully executable toolset. You'll find that managing complex streaming pipelines is straightforward because you can inspect every piece of data going in and out, and you can modify it as it flows through the system.

To start, you need to know what data you're dealing with. You can use list_sources to check all the external systems connected to RisingWave; this shows you exactly where your raw data is coming from, whether it's an S3 bucket or another database connection. Next, if you want a full picture of your setup, list_tables gives you a straightforward list of every standard, user-defined table in the schema.

You can also check out what processed data ends up as materialized views; running list_materialized_views shows you the names and current status of all those complex, pre-calculated datasets.

Data doesn't just appear magically. The server lets you track where it goes once it’s done being processed. You can run list_sinks to view every configured external destination—that’s where your final output gets written—and list_sources helps confirm the input endpoints. If you need a complete inventory of the pipeline's components, you've got all the details right here.

Once you know what data exists and where it flows, you can start manipulating it. You have full power over running SQL statements. The execute_sql tool runs any full DDL or DML statement against your database. This means you can execute things like CREATE, SELECT, or INSERT commands directly through chat.

You're not limited to just reading data; you can write and change it live.

For raw, real-time input, the ingest_events tool lets you feed streaming JSON records straight into a specific table for immediate processing. You don't need separate scripts or endpoints; you just tell your agent what data to send, and it handles the stream ingestion process instantly.

Basically, this server gives you total visibility and control. You run execute_sql to change the schema or write new records. You use ingest_events to push raw JSON objects into a table as they arrive. If you need to know what external systems feed the system, you check list_sources. To see where your cleaned data goes next, you check list_sinks.

And if you wanna audit all the complex datasets that have been pre-calculated for you, just run list_materialized_views.