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Servers/Databases/StarRocks
DatabasesOfficial
S

StarRocks

Interact with StarRocks

185 starsUpdated Aug 21, 2026
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Overview

The StarRocks MCP Server acts as a bridge between AI assistants and StarRocks databases. It allows for direct SQL execution, database exploration, data visualization via charts, and retrieving detailed schema/data overviews without requiring complex client-side setup. - Direct SQL Execution: Run SELECT queries (read_query) and DDL/DML commands (write_query). - Database Exploration: List databases and tables, retrieve table schemas (starrocks:// resources). - System Information: Access internal StarRocks metrics and states via the proc:// resource path. - Detailed Overviews: Get comprehensive summaries of tables (table_overview) or entire databases (db_overview), including column definitions, row counts, and sample data. - Data Visualization: Execute a query and generate a Plotly chart directly from the results (query_and_plotly_chart). - Intelligent Caching: Table and database overviews are cached in memory to speed up repeated requests. Cache can be bypassed when needed. - Flexible Co

Installation

uv run --with mcp-server-starrocks mcp-server-starrocks --help

Configuration

{
  "mcpServers": {
    "mcp-server-starrocks": {
      "url": "http://localhost:8000/mcp"
    }
  }

Capabilities

  • Run read-only SQL queries

    execute `SELECT`, `SHOW`, or `DESCRIBE` statements via `read_query` and optionally save large results to a file.

  • Execute DDL/DML commands

    run `CREATE`, `INSERT`, `UPDATE`, or `DELETE` operations with `write_query` and get a row-affected confirmation.

  • Explore database schemas

    list databases, tables, and retrieve `SHOW CREATE TABLE` definitions through `starrocks://` resources.

  • Get table and database overviews

    use `table_overview` or `db_overview` to fetch column definitions, row counts, and sample rows, with in-memory caching.

  • Visualize query results as charts

    supply a SQL query and a Plotly expression to `query_and_plotly_chart` and receive a chart image.

  • Inspect cluster health and hotspots

    identify frequently accessed tables with `top_hot_tables` or low-health tables with `top_bad_tables`, and access internal system metrics via `proc://` resources.

Tags

  • database
  • sql
  • analytics

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