StarRocks MCP Server Integration

What does “StarRocks” MCP Server do?

The StarRocks MCP (Model Context Protocol) Server acts as an intelligent bridge between AI assistants and StarRocks databases. It provides seamless access for AI agents to execute SQL queries, explore databases, retrieve schema and data overviews, and visualize data using charts—all without requiring complex client-side setup. By exposing StarRocks database resources and actions as MCP primitives, the server enables tasks such as listing tables, running SELECT or DDL/DML commands, and generating comprehensive summaries at both the table and database level. Additionally, intelligent in-memory caching speeds up repeated requests, and flexible environment configuration makes integration into developer workflows straightforward. This enhances developer productivity when building AI-powered data tools, analytics agents, or database management solutions.

List of Prompts

No prompt templates are explicitly mentioned in the repository.

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List of Resources

• starrocks://
  Allows clients to list databases and tables, as well as retrieve table schemas from the connected StarRocks instance.
• proc://
  Provides access to internal StarRocks metrics and system states, exposing system-level information as resources.
• Table Overview
  Offers comprehensive summaries of individual tables, including column definitions, row counts, and sample data.
• Database Overview
  Delivers detailed summaries of entire databases, covering schema and high-level data insights.

List of Tools

• read_query
  Executes SELECT SQL queries against the StarRocks database and returns results.
• write_query
  Runs DDL/DML commands (such as INSERT, UPDATE, DELETE, CREATE, etc.) for database modification.
• table_overview
  Generates a summary of a specified table, including schema, statistics, and sample content.
• db_overview
  Produces an overview of a specified database, summarizing its structure and data.
• query_and_plotly_chart
  Runs a query and automatically creates a Plotly chart from the returned results, enabling data visualization.

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Use Cases of this MCP Server

• Database Management
  Directly execute and manage StarRocks SQL queries, DDL, and DML operations to handle schema changes, data inserts, and updates through AI assistants.
• Schema and Data Exploration
  Quickly explore databases, tables, and their schemas, aiding developers in understanding data models and relationships without manual queries.
• Automated Reporting and Visualization
  Generate charts and visual representations of query results instantly, making analytics and reporting more interactive within AI workflows.
• System Monitoring
  Access internal StarRocks metrics and states for monitoring database health, performance, and debugging.
• AI-Assisted Data Analysis
  Use AI assistants to summarize, interpret, or provide insights on data and schema overviews, improving productivity and decision-making.

How to set it up

Windsurf

1. Ensure uv is installed and StarRocks MCP server package is available.
2. Locate your Windsurf configuration file.
3. Add the StarRocks MCP Server configuration in the mcpServers object:

   {
     "mcpServers": {
       "mcp-server-starrocks": {
         "command": "uv",
         "args": ["run", "--with", "mcp-server-starrocks", "mcp-server-starrocks"],
         "env": {
           "STARROCKS_HOST": "localhost",
           "STARROCKS_PORT": "9030",
           "STARROCKS_USER": "root",
           "STARROCKS_PASSWORD": "",
           "STARROCKS_DB": "",
           "STARROCKS_OVERVIEW_LIMIT": "20000",
           "STARROCKS_MYSQL_AUTH_PLUGIN":"mysql_clear_password"
         }
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the MCP server is running and accessible.

Claude

1. Verify Node.js and uv are installed.
2. Open Claude’s MCP configuration file.
3. Add the following to the mcpServers object:

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

4. Start the server in streamable HTTP mode:

   export MCP_TRANSPORT_MODE=streamable-http
   uv run mcp-server-starrocks
   

5. Confirm that Claude recognizes the new MCP server.

Cursor

1. Install uv and StarRocks MCP server locally or as a package.
2. Edit the Cursor MCP configuration.
3. For local development, use:

   {
     "mcpServers": {
       "mcp-server-starrocks": {
         "command": "uv",
         "args": [
           "--directory",
           "path/to/mcp-server-starrocks",
           "run",
           "mcp-server-starrocks"
         ],
         "env": {
           "STARROCKS_HOST": "localhost",
           "STARROCKS_PORT": "9030",
           "STARROCKS_USER": "root",
           "STARROCKS_PASSWORD": "",
           "STARROCKS_DB": "",
           "STARROCKS_OVERVIEW_LIMIT": "20000",
           "STARROCKS_MYSQL_AUTH_PLUGIN":"mysql_clear_password"
         }
       }
     }
   }
   

4. Save and restart Cursor.
5. Ensure the MCP server is discoverable and functional.

Cline

1. Install prerequisites (uv, StarRocks MCP server).
2. Edit the Cline configuration file.
3. Add the MCP server using the recommended Streamable HTTP integration:

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

4. Run the following to start the server:

   export MCP_TRANSPORT_MODE=streamable-http
   uv run mcp-server-starrocks
   

5. Test the setup within Cline’s UI or command-line interface.

Securing API Keys with Environment Variables

Store sensitive data such as database credentials using environment variables in your MCP server configuration. Here’s an example:

{
  "mcpServers": {
    "mcp-server-starrocks": {
      "command": "uv",
      "args": ["run", "--with", "mcp-server-starrocks", "mcp-server-starrocks"],
      "env": {
        "STARROCKS_HOST": "${STARROCKS_HOST}",
        "STARROCKS_USER": "${STARROCKS_USER}",
        "STARROCKS_PASSWORD": "${STARROCKS_PASSWORD}"
      },
      "inputs": {
        "STARROCKS_DB": "analytics"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "starrocks": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “starrocks” to whatever the actual name of your MCP server is (e.g., “github-mcp”, “weather-api”, etc.) and replace the URL with your own MCP server URL.

Overview

Our opinion

StarRocks MCP Server is a well-scoped, production-grade MCP implementation for StarRocks database integration. It provides strong resource and tool coverage for data-driven workflows, though lacks prompt templates and sampling/roots features. Its documentation is solid, setup is well-explained, and it supports secure configuration.

Overall, we’d rate this MCP server a 7/10 for general usability and completeness for StarRocks-driven AI workflows.

MCP Score