MCP + SQL

Model Context Protocol with a Streaming Database

Expose live SQL views as MCP tools for AI agents. RisingWave's PostgreSQL compatibility makes every materialized view discoverable through the Model Context Protocol — enabling agents to query fresh business data using natural language.

Start Free Documentation

Zero-Config

MCP Integration

Connect any PostgreSQL MCP server to RisingWave using standard connection strings — views appear automatically

Auto-Discover

Schema Introspection

MCP servers discover materialized views through information_schema — no manual tool definitions needed

Sub-100ms

Tool Call Latency

Pre-computed views deliver instant results for agent tool calls, enabling real-time conversations

View-as-API

Pattern

Each materialized view becomes a self-describing API endpoint that agents understand automatically

Why MCP

What is the Model Context Protocol and why does it matter for databases?

The Model Context Protocol (MCP) standardizes how AI agents discover, connect to, and query external data sources. For databases, MCP eliminates the need to build custom API wrappers — agents automatically discover available tables and views, understand their schemas, and construct queries using natural language.

Factor	Static Database + MCP	RisingWave + MCP
Data Freshness	Static snapshots	Continuously updated
Tool Discovery	Manual definitions	Auto-discovered via schema
Query Interface	Custom API wrappers	Standard PostgreSQL SQL
Maintenance	API code per tool	SQL views only

→MCP lets agents auto-discover what data exists and what queries make sense
→Agents translate user intent into SQL queries against discovered views
→Column names, types, and descriptions serve as automatic context for query generation
→MCP + PostgreSQL protocol means one connection serves both AI agents and traditional applications

Use Cases

What can AI agents do with MCP-connected materialized views?

With MCP-connected materialized views, AI agents can answer real-time business questions, monitor live metrics, detect anomalies, and trigger actions — all through natural language conversations backed by continuously-updated SQL views that reflect the current state of your data.

Real-Time Business Intelligence

Ask 'What are today's top-selling products?' and the agent queries a live materialized view that aggregates sales streams in real-time

Live Monitoring and Alerting

Agents query streaming metrics views to detect anomalies, report on SLA compliance, and surface infrastructure issues conversationally

Customer Context Retrieval

Support agents query per-customer materialized views to get real-time order status, behavioral signals, and personalized recommendations

Operational Decision Support

Operations teams ask natural language questions about inventory, supply chain, or logistics — backed by views that process live event streams

Integration

How does RisingWave integrate with MCP for agent-ready data?

RisingWave speaks PostgreSQL protocol natively. Any PostgreSQL MCP server connects to RisingWave, discovers materialized views through information_schema, and exposes them as queryable tools. Because views update continuously from streaming sources, agents always access current data — not stale snapshots.

→Connect any PostgreSQL MCP server to RisingWave using standard connection strings
→Materialized views appear as queryable tools automatically — no manual registration
→Views update continuously from streaming sources so MCP tool calls always return current data
→Name views descriptively and agents understand what data they provide
→Use RisingWave's built-in role-based access control to limit which views agents can discover

Frequently Asked Questions

What is the Model Context Protocol (MCP)?

How does RisingWave work with MCP?

Do I need to build custom MCP tools for each materialized view?

What makes streaming data better than static data for MCP?

Ready to connect MCP to RisingWave?

Expose live materialized views as MCP tools for your AI agents.

Connect MCP to RisingWave