StarRocks MCP Server – A server tool for MCP launched by StarRocks

What is StarRocks MCP Server?

StarRocks MCP Server is a tool developed by StarRocks that connects AI assistants with the StarRocks database, providing powerful data interaction capabilities for AI applications. It supports direct SQL execution (including SELECT queries and DDL/DML commands), allows listing databases and tables, retrieving table schemas, and accessing StarRocks’ internal metrics and status via proc:// paths. It can generate detailed overviews of tables or databases and supports data visualization by creating Plotly charts directly from query results.

Key Features of StarRocks MCP Server

• Direct SQL Execution: Supports running SELECT queries (read_query) and DDL/DML commands (write_query), enabling AI assistants to interact with the database directly.

• Database Exploration: Lists databases and tables and retrieves table structures via starrocks:// resources, helping AI assistants quickly understand the database schema.

• System Information Access: Accesses StarRocks’ internal metrics and system status via proc:// resource paths, providing system-level insights to AI assistants.

• Detailed Overviews: Retrieves comprehensive overviews of a specific table (table_overview) or the entire database (db_overview), including column definitions, row counts, and sample data.

• Data Visualization: Executes queries and generates Plotly charts from results via query_and_plotly_chart, enhancing data readability and analytical insight.

• Intelligent Caching: Overviews of tables and databases are cached in memory to accelerate repeated requests, with options to bypass cache when needed.

• Flexible Configuration: Connection details and behavior can be customized via environment variables, allowing users to configure according to specific needs.

Technical Architecture of StarRocks MCP Server

• Architecture Design:

  • MCP Host: The language model-based application, such as Claude Desktop or an AI-integrated IDE, through which the user initiates queries or actions.

  • MCP Client: Manages the connection logic, establishes communication with the MCP Server, and handles coordination.

  • MCP Server: Exposes specific capabilities like file access or database operations and provides a set of tools, resources, or prompts through a standardized interface.

• Data Interaction Mechanism:

  • Transport Layer: Supports two communication modes for different scenarios:

    • Stdio Transport: Suitable for local inter-process communication.

    • HTTP + SSE Transport: The server sends messages to the client via Server-Sent Events (SSE), and the client sends requests via HTTP POST, ideal for remote network communication.

  • Message Exchange: All communication uses the JSON-RPC 2.0 protocol, supporting multiple message types such as Request, which expects a response.

  • Stateful Bidirectional RPC Model: MCP functions as a stateful, bidirectional RPC system, combining event-driven and request-response mechanisms.

Project Repository

• GitHub Repository: https://github.com/StarRocks/mcp-server-starrocks

Application Scenarios of StarRocks MCP Server

• Multidimensional Analytical Reports: Leveraging StarRocks’ MPP framework and vectorized execution engine, it supports flexible multidimensional reports suitable for scenarios like user behavior analysis, profiling, tagging, segmentation, cross-topic business analysis, financial reporting, and system monitoring.

• Seamless Integration Between AI Applications and Databases: Enables AI applications to access StarRocks databases directly without complex manual management. AI models can execute SQL queries, retrieve schema and statistics, and seamlessly integrate with databases.

• Intelligent Data Exploration and Visualization: AI assistants can explore database structures, run queries, and generate visualization charts via StarRocks MCP Server, providing more intuitive and readable analytical results.

• Real-time Data Warehouse Construction: Leveraging StarRocks’ Primary-Key model for real-time data updates and lightning-fast queries, it supports second-level synchronization with TP databases to build real-time data warehouses.