About MCP + openGauss
MCP server for connecting AI agents to openGauss databases. It exposes database tools that Claude Desktop can use to run SQL and support question answering.
Key features
- 01openGauss 数据库连接
- 02通过工具执行 SQL
- 03与 Claude Desktop 配合问答
Use cases
- 01通过 Claude Desktop 查询 openGauss 数据
- 02构建基于数据库的 Agent 应用
MCP support
Exposes openGauss database tools to MCP clients, enabling SQL execution through the server.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP + openGauss
随着AI从静态推理向动态交互演进，智能体（Agent）逐渐成为焦点。Agent不仅能够调用LLM进行推理，还能访问数据库、调用API、执行任务。然而，当前LLM和Agent之间缺乏标准化交互协议, 每个新数据源都需要自定义实现，使得真正互联的系统难以扩展。MCP(Model Context Protocol, 模型上下文协议)解决了这一挑战，MCP是为LLM和Agent系统设计的标准化交互框架，使LLM可以与外部数据库、API和工具进行高效交互。
openGauss + MCP + LLM 架构
图 1 openGauss + MCP + LLM 架构
快速搭建openGauss + MCP + LLM的AI Agent应用
环境准备
- 安装python3环境，安装uv。
- 通过容器部署并启动openGauss数据库。(openGauss官网：学习->文档->最新开发版本->安装指南->容器镜像安装）
- 下载Claude Desktop配合MCP协议进行问答操作。
获取openGauss_mcp_server源码
访问链接, 获取openGauss_mcp_server源码，当前版本为（0.1.0）。
配置参数
- 打开Claude Desktop设置，编辑配置文件。
图 2 Claude Desktop配置页面
- 通过Edit Config增加配置
{
"mcpServers": {
"openGauss": {
"command": "uv",
"args": [
"--directory",
"path/to/openGauss_mcp_server",
"run",
"server.py"
],
"env": {
"OPENGAUSS_HOST": "localhost",
"OPENGAUSS_PORT": "8888",
"OPENGAUSS_USER": "your_username",
"OPENGAUSS_PASSWORD": "your_password",
"OPENGAUSS_DBNAME": "your_database"
}
}
}
}
AI服务集成
重新启动Claude Desktop
可以看到可用MCP Tool, 执行sql通过openGauss server
图 3 Claude Desktop可用MCP Tool
使用Cluade Desktop通过openGauss进行问答
图 4 Claude Desktop问答演示
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.