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MCP Server Mariadb Vector

@DavidRamosSal

Repository

Basic information

Category

Databases

License

LGPL-2.1 license

Transports

stdio

Author

DavidRamosSal

About MCP Server Mariadb Vector

MCP server for MariaDB

#mcp-server#mariadb#vector

MCP Server Mariadb Vector config

Add this server to your MCP-compatible client using the configuration below.

{
  "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 tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Frequently asked questions

1

What MariaDB version is required?

MariaDB 11.7 or higher with vector support is required.

2

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.

3

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`.

4

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.

5

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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