MCP MariaDB Server

The MCP MariaDB Server provides a Model Context Protocol (MCP) interface for managing and querying MariaDB databases, supporting both standard SQL operations and advanced vector/embedding-based search. Designed for use with AI assistants, it enables seamless integration of AI-driven data workflows with relational and vector databases.

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Table of Contents

• Overview
• Core Components
• Available Tools
• Embeddings & Vector Store
• Configuration & Environment Variables
• Installation & Setup
• Usage Examples
• Integration - Claude desktop/Cursor/Windsurf
• Logging
• Testing

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Overview

The MCP MariaDB Server exposes a set of tools for interacting with MariaDB databases and vector stores via a standardized protocol. It supports:

• Listing databases and tables
• Retrieving table schemas
• Executing safe, read-only SQL queries
• Creating and managing vector stores for embedding-based search
• Integrating with embedding providers (currently OpenAI, Gemini, and HuggingFace) (optional)

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Core Components

• server.py: Main MCP server logic and tool definitions.
• config.py: Loads configuration from environment and .env files.
• embeddings.py: Handles embedding service integration (OpenAI).
• tests/: Manual and automated test documentation and scripts.

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Available Tools

Standard Database Tools

• list_databases

  • Lists all accessible databases.
  • Parameters: None

• list_tables

  • Lists all tables in a specified database.
  • Parameters: database_name (string, required)

• get_table_schema

  • Retrieves schema for a table (columns, types, keys, etc.).
  • Parameters: database_name (string, required), table_name (string, required)

• get_table_schema_with_relations

  • Retrieves schema with foreign key relations for a table.
  • Parameters: database_name (string, required), table_name (string, required)

• execute_sql

  • Executes a read-only SQL query (SELECT, SHOW, DESCRIBE).
  • Parameters: sql_query (string, required), database_name (string, optional), parameters (list, optional)
  • Note: Enforces read-only mode if MCP_READ_ONLY is enabled.

• create_database

  • Creates a new database if it doesn't exist.
  • Parameters: database_name (string, required)

Vector Store & Embedding Tools (optional)

Note: These tools are only available when EMBEDDING_PROVIDER is configured. If no embedding provider is set, these tools will be disabled.

• create_vector_store

  • Creates a new vector store (table) for embeddings.
  • Parameters: database_name, vector_store_name, model_name (optional), distance_function (optional, default: cosine)

• delete_vector_store

  • Deletes a vector store (table).
  • Parameters: database_name, vector_store_name

• list_vector_stores

  • Lists all vector stores in a database.
  • Parameters: database_name

• insert_docs_vector_store

  • Batch inserts documents (and optional metadata) into a vector store.
  • Parameters: database_name, vector_store_name, documents (list of strings), metadata (optional list of dicts)

• search_vector_store

  • Performs semantic search for similar documents using embeddings.
  • Parameters: database_name, vector_store_name, user_query (string), k (optional, default: 7)

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Embeddings & Vector Store

Overview

The MCP MariaDB Server provides optional embedding and vector store capabilities. These features can be enabled by configuring an embedding provider, or completely disabled if you only need standard database operations.

Supported Providers

• OpenAI
• Gemini
• Open models from Huggingface

Configuration

• EMBEDDING_PROVIDER: Set to openai, gemini, huggingface, or leave unset to disable
• OPENAI_API_KEY: Required if using OpenAI embeddings
• GEMINI_API_KEY: Required if using Gemini embeddings
• HF_MODEL: Required if using HuggingFace embeddings (e.g., "intfloat/multilingual-e5-large-instruct" or "BAAI/bge-m3")

Model Selection

• Default and allowed models are configurable in code (DEFAULT_OPENAI_MODEL, ALLOWED_OPENAI_MODELS)
• Model can be selected per request or defaults to the configured model

Vector Store Schema

A vector store table has the following columns:

• id: Auto-increment primary key
• document: Text of the document
• embedding: VECTOR type (indexed for similarity search)
• metadata: JSON (optional metadata)

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Configuration & Environment Variables

All configuration is via environment variables (typically set in a .env file):

Variable	Description	Required	Default
DB_HOST	MariaDB host address	Yes	localhost
DB_PORT	MariaDB port	No	3306
DB_USER	MariaDB username	Yes
DB_PASSWORD	MariaDB password	Yes
DB_NAME	Default database (optional; can be set per query)	No
DB_CHARSET	Character set for database connection (e.g., cp1251)	No	MariaDB default
MCP_READ_ONLY	Enforce read-only SQL mode (true/false)	No	true
MCP_MAX_POOL_SIZE	Max DB connection pool size	No	10
EMBEDDING_PROVIDER	Embedding provider (openai/gemini/huggingface)	No	None(Disabled)
OPENAI_API_KEY	API key for OpenAI embeddings	Yes (if EMBEDDING_PROVIDER=openai)
GEMINI_API_KEY	API key for Gemini embeddings	Yes (if EMBEDDING_PROVIDER=gemini)
HF_MODEL	Open models from Huggingface	Yes (if EMBEDDING_PROVIDER=huggingface)
ALLOWED_ORIGINS	Comma-separated list of allowed origins	No	Long list of allowed origins corresponding to local use of the server
ALLOWED_HOSTS	Comma-separated list of allowed hosts	No	localhost,127.0.0.1

Note that if using 'http' or 'sse' as the transport, configuring authentication is important for security if you allow connections outside of localhost. Because different organizations use different authentication methods, the server does not provide a default authentication method. You will need to configure your own authentication method. Thankfully FastMCP provides a simple way to do this starting with version 2.12.1. See the FastMCP documentation for more information. We have provided an example configuration below.

Example .env file

With Embedding Support (OpenAI):

DB_HOST=localhost
DB_USER=your_db_user
DB_PASSWORD=your_db_password
DB_PORT=3306
DB_NAME=your_default_database

MCP_READ_ONLY=true
MCP_MAX_POOL_SIZE=10

EMBEDDING_PROVIDER=openai
OPENAI_API_KEY=sk-...
GEMINI_API_KEY=AI...
HF_MODEL="BAAI/bge-m3"

Without Embedding Support:

DB_HOST=localhost
DB_USER=your_db_user
DB_PASSWORD=your_db_password
DB_PORT=3306
DB_NAME=your_default_database
MCP_READ_ONLY=true
MCP_MAX_POOL_SIZE=10

Example Authentication Configuration: This configuration uses external web authentication via GitHub or Google. If you have internal JWT authentication (desired for organizations who manage their own services), you can use the JWT provider instead.

# GitHub OAuth
export FASTMCP_SERVER_AUTH=fastmcp.server.auth.providers.github.GitHubProvider
export FASTMCP_SERVER_AUTH_GITHUB_CLIENT_ID="Ov23li..."
export FASTMCP_SERVER_AUTH_GITHUB_CLIENT_SECRET="github_pat_..."

# Google OAuth
export FASTMCP_SERVER_AUTH=fastmcp.server.auth.providers.google.GoogleProvider
export FASTMCP_SERVER_AUTH_GOOGLE_CLIENT_ID="123456.apps.googleusercontent.com"
export FASTMCP_SERVER_AUTH_GOOGLE_CLIENT_SECRET="GOCSPX-..."

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Installation & Setup

Requirements

• Python 3.11 (see .python-version)
• uv (dependency manager; install instructions)
• MariaDB server (local or remote)

Steps

1. Clone the repository

2. Install uv (if not already):

   pip install uv
   

3. Install dependencies

   uv lock
   uv sync
   

4. Create .env in the project root (see Configuration)

5. Run the server

   Standard Input/Output (default):

   uv run server.py
   

   SSE Transport:

   uv run server.py --transport sse --host 127.0.0.1 --port 9001
   

   HTTP Transport (streamable HTTP):

   uv run server.py --transport http --host 127.0.0.1 --port 9001 --path /mcp
   

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Usage Examples

Standard SQL Query

{
  "tool": "execute_sql",
  "parameters": {
    "database_name": "test_db",
    "sql_query": "SELECT * FROM users WHERE id = %s",
    "parameters": [123]
  }
}

Create Vector Store

{
  "tool": "create_vector_store",
  "parameters": {
    "database_name": "test_db",
    "vector_store_name": "my_vectors",
    "model_name": "text-embedding-3-small",
    "distance_function": "cosine"
  }
}

Insert Documents into Vector Store

{
  "tool": "insert_docs_vector_store",
  "parameters": {
    "database_name": "test_db",
    "vector_store_name": "my_vectors",
    "documents": ["Sample text 1", "Sample text 2"],
    "metadata": [{"source": "doc1"}, {"source": "doc2"}]
  }
}

Semantic Search

{
  "tool": "search_vector_store",
  "parameters": {
    "database_name": "test_db",
    "vector_store_name": "my_vectors",
    "user_query": "What is the capital of France?",
    "k": 5
  }
}

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Integration - Claude desktop/Cursor/Windsurf/VSCode

Option 1: Direct Command (stdio)

{
  "mcpServers": {
    "MariaDB_Server": {
      "command": "uv",
      "args": [
        "--directory",
        "path/to/mariadb-mcp-server/",
        "run",
        "server.py"
        ],
        "envFile": "path/to/mcp-server-mariadb-vector/.env"      
    }
  }
}

Option 2: SSE Transport

{
  "servers": {
    "mariadb-mcp-server": {
      "url": "http://{host}:9001/sse",
      "type": "sse"
    }
  }
}

Option 3: HTTP Transport

{
  "servers": {
    "mariadb-mcp-server": {
      "url": "http://{host}:9001/mcp",
      "type": "streamable-http"
    }
  }
}

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Logging

• Logs are written to logs/mcp_server.log by default.
• Log messages include tool calls, configuration issues, embedding errors, and client requests.
• Log level and output can be adjusted in the code (see config.py and logger setup).

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Testing

• Tests are located in the src/tests/ directory.
• See src/tests/README.md for an overview.
• Tests cover both standard SQL and vector/embedding tool operations.