MCP Server Mariadb Vector

@DavidRamosSal

代码仓库

基本信息

分类

数据库

许可证

LGPL-2.1 license

传输方式

stdio

作者

DavidRamosSal

关于 MCP Server Mariadb Vector

MCP server for MariaDB

#mcp-server #mariadb #vector

MCP Server Mariadb Vector 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-server-mariadb-vector": {
      "command": "uv",
      "args": [
        "run",
        "--dir",
        "path/to/mcp-server-mariadb-vector/",
        "--env-file",
        "path/to/mcp-server-mariadb-vector/.env",
        "mcp_server_mariadb_vector"
      ]
    }
  }
}

MCP Server Mariadb Vector 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is mcp-server-mariadb-vector?

The mcp-server-mariadb-vector server provides tools that LLM agents can use to interact with a MariaDB database with vector support. It gives users a natural language interface to store and query data, and is compatible with any Model Context Protocol (MCP) client, such as Claude Desktop, Cursor, and Windsurf.

How to use mcp-server-mariadb-vector?

You need a running MariaDB instance (version 11.7 or higher) with vector support. Configure environment variables for database connection, embedding provider, and API key. Then run the server either as a Python package with uv or as a Docker container. Finally, add the server to your MCP client’s configuration file (e.g., claude_desktop_config.json) using the provided uv run command or SSE URL.

Key features of mcp-server-mariadb-vector

Create, delete, and list vector stores in MariaDB.
Add documents with optional metadata to a vector store.
Query a vector store using semantic search.
Uses OpenAI’s embedding models (e.g., text-embedding-3-small).
Works with any MCP-compatible client or framework.

Use cases of mcp-server-mariadb-vector

Provide contextual knowledge base to LLM agent conversations.
Store and retrieve conversation history with LLM agents.
Enable semantic search over personal or domain‑specific document collections.

常见问题

What MariaDB version is required?

MariaDB 11.7 or higher with vector support is required.

How can I run the server?

You can run the server using `uv` (Python package) or as a Docker container. Both methods are described in the README.

Which embedding provider is supported?

Currently, the server supports OpenAI embedding models. The default provider is `openai` and the default model is `text-embedding-3-small`.

How do I connect the server to Claude Desktop, Cursor, or Windsurf?

Add an entry for the server to the client’s MCP configuration file (e.g., `claude_desktop_config.json`) with the `uv run` command and a `.env` file, or use the SSE URL `http://localhost:8000/sse` if running as a Docker container.

What environment variables are required?

Required variables include `MARIADB_HOST`, `MARIADB_PORT`, `MARIADB_USER`, `MARIADB_PASSWORD`, `MARIADB_DATABASE`, `EMBEDDING_PROVIDER`, `EMBEDDING_MODEL`, and `OPENAI_API_KEY`. Defaults are provided for host (127.0.0.1), port (3306), provider (openai), and model (text-embedding-3-small).

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