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Explore and manage your streaming data pipeline conversationally

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What is RisingWave MCP?

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RisingWave is a Postgres-compatible streaming database that maintains materialized views incrementally as events arrive. Its MCP server connects an agent to a running instance over the Postgres wire protocol and exposes the catalog as tools: list databases, schemas, tables, and materialized views, describe a table's columns and types, and inspect the sources and sinks that wire the database to Kafka topics, change data capture streams, and downstream systems. Beyond exploration it runs queries and, where the connected role permits, executes statements that create or drop materialized views, sources, and sinks. That turns a chat client into a workable interface for iterating on a streaming pipeline: ask what data is available, inspect a schema, draft a view, create it, then query the maintained result a moment later instead of waiting for a scheduled job. The server is open source and works against self-hosted RisingWave as well as the managed cloud service. It suits data engineers prototyping streaming transformations, analysts who want conversational access to continuously updated aggregates, and teams building agents that need current numbers rather than a nightly snapshot. Permissions follow the database role in the connection string, so restricting an agent to reads is a matter of pointing it at a read-only user.

Key Features

List databases, schemas, tables, and materialized viewsDescribe table structure, columns, and data typesRun SQL queries against continuously maintained resultsCreate and drop materialized views, sources, and sinksInspect connectors linking RisingWave to Kafka and CDC streamsConnects over the Postgres wire protocol with role-based permissions

Pricing

Freemium

RisingWave MCP offers a generous free tier with optional paid upgrades for advanced features.

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RisingWave MCP FAQ

How does RisingWave MCP help a data engineer prototype streaming transformations?

RisingWave MCP allows a data engineer to ask what data is available, inspect a schema, draft a materialized view, create it, and then query the maintained result a moment later, all through a chat interface. This turns conversational iteration into a viable workflow without waiting for a scheduled job.

How does RisingWave MCP compare to TimescaleDB for managing streaming data?

Unlike TimescaleDB, which is designed for time-series data using hypertables and continuous aggregates, RisingWave MCP is built as a Postgres-compatible streaming database that incrementally maintains materialized views as events arrive. RisingWave MCP exposes the full catalog as tools and connects agents conversationally over the Postgres wire protocol.

Which teams benefit most from RisingWave MCP?

Data engineers prototyping streaming transformations and analysts who want conversational access to continuously updated aggregates benefit most from RisingWave MCP. Teams building agents that need current numbers rather than a nightly snapshot also find it highly useful.

How is RisingWave MCP priced?

RisingWave MCP is available on a free tier, with paid plans for more usage and additional features.

Can RisingWave MCP inspect the sources and sinks wiring data to Kafka topics and change data capture streams?

Yes, RisingWave MCP can inspect the sources and sinks that wire the database to Kafka topics, change data capture streams, and downstream systems. This allows users to explore the full streaming pipeline conversationally.

Does RisingWave MCP support both self-hosted instances and a managed cloud service?

Yes, RisingWave MCP works against self-hosted RisingWave instances as well as the managed cloud service. The server is open source and connects over the Postgres wire protocol.

Why would a team choose RisingWave MCP for building conversational agents that need real-time data?

RisingWave MCP exposes the catalog as tools and allows agents to list databases, schemas, tables, materialized views, and query results conversationally. Because the database incrementally maintains materialized views, agents always get current numbers instead of a nightly snapshot.

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