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.

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

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)

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.

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)

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)

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
`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)

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

Installation & Setup

Requirements

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

Steps

Clone the repository
Install uv (if not already):
```
pip install uv
```

Install dependencies

uv pip compile pyproject.toml -o uv.lock

uv pip sync uv.lock

Create .env in the project root (see Configuration)
Run the server
```
python server.py
```
Adjust entry point if needed (e.g., main.py)

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
  }
}

Integration - Claude desktop/Cursor/Windsurf/VSCode

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

or If already running MCP server

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

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).

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.

MCP MariaDB Server

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

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)

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.

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)

Embeddings & Vector Store

Overview

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)

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
`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)

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

Installation & Setup

Requirements

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

Steps

Clone the repository
Install uv (if not already):
```
pip install uv
```

Install dependencies

uv pip compile pyproject.toml -o uv.lock

uv pip sync uv.lock

Create .env in the project root (see Configuration)
Run the server
```
python server.py
```
Adjust entry point if needed (e.g., main.py)

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
  }
}

Integration - Claude desktop/Cursor/Windsurf/VSCode

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

or If already running MCP server

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

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).

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.

MCP MariaDB Server

MCP MariaDB Server

Table of Contents

Overview

Core Components

Available Tools

Standard Database Tools

Vector Store & Embedding Tools (optional)

Embeddings & Vector Store

Overview

Supported Providers

Configuration

Model Selection

Vector Store Schema

Configuration & Environment Variables

Example .env file

Installation & Setup

Requirements

Steps

Usage Examples

Standard SQL Query

Create Vector Store

Insert Documents into Vector Store

Semantic Search

Integration - Claude desktop/Cursor/Windsurf/VSCode

Logging

Testing

MCP MariaDB Server

Table of Contents

Overview

Core Components

Available Tools

Standard Database Tools

Vector Store & Embedding Tools (optional)

Embeddings & Vector Store

Overview

Supported Providers

Configuration

Model Selection

Vector Store Schema

Configuration & Environment Variables

Example .env file

Installation & Setup

Requirements

Steps

Usage Examples

Standard SQL Query

Create Vector Store

Insert Documents into Vector Store

Semantic Search

Integration - Claude desktop/Cursor/Windsurf/VSCode

Logging

Testing

Example `.env` file

Example `.env` file