ShortlistFirst-party MCP

MariaDB MCP

by MariaDB Foundation

Updated October 20, 2026

Data & DatabasesProfessional ServicesSoftware & Technology

MCP100 verdict

Not currently recommended. First-party or well-evidenced implementation identity, meaningful task value, maintained setup evidence, and no material admission veto were found in reviewed sources.

MCP100 Assessment

Editorial verdict

First-party or well-evidenced implementation identity, meaningful task value, maintained setup evidence, and no material admission veto were found in reviewed sources.

Reviewed October 19, 2026

Task Fit

Mapped

Working with structured data

Trust

Status warning

Shortlist evidence is sufficient for identity, setup and task value; authenticated runtime tool-schema measurement and exhaustive security certification remain out of scope.

Maintenance

Date unavailable

No dated repository or package update is currently stored.

Popularity

Signals available

Visible usage signals are shown for discovery only, not as proof of safety.

Best for

Inspect and query MariaDB databases, with optional vector-search workflows
Inspect and query MariaDB databases, with optional vector-search workflows.

Not ideal for

Database access is sensitive. MCP_READ_ONLY is only a best-effort application guard; the project explicitly recommends a dedicated database user with least privileges for true read-only protection. HTTP/SSE deployments require separately configured authentication when exposed beyond localhost.

Avoid if

Database access is sensitive. MCP_READ_ONLY is only a best-effort application guard; the project explicitly recommends a dedicated database user with least privileges for true read-only protection. HTTP/SSE deployments require separately configured authentication when exposed beyond localhost.

Trade-offs

Shortlist evidence is sufficient for identity, setup and task value; authenticated runtime tool-schema measurement and exhaustive security certification remain out of scope.

Evidence scope

Evidence

Current source snapshot

Popularity

209 MCP repository stars

No MCP-specific package metric is stored

Repository identity: MariaDB/mcp

Stars observed October 20, 2026 · Downloads observed October 20, 2026

Current first-party MariaDB MCP implementation repository.

The current repository defines project name `mariadb-server`, but no corresponding public PyPI project was found in the reviewed registry check. No attributable public package-download metric is published.

Maintenance

Publisher activity observed October 20, 2026

Current source snapshot

Trust review

Source relationship: First-party MCP

Reviewed October 19, 2026

Sources

GitHub repository ↗

Evidence-backed setup

Install & Agents

Choose your MCP client. A client connects to the MCP server; the language model is selected inside that client. Only stored, source-backed configurations are shown.

Claude Code

Transport not recorded

No official setup found

Complete client-specific setup, verification and removal are not documented in this reviewed source.

Evidence & methodology

Publisher-documented setup is not the same as MCP100 execution testing. “MCP100 logic checked” means the stored configuration shape was reviewed, not that third-party MCP software was run.

Evidence checked 2026-10-20

Evidence 1 ↗

Reviewed setup

Type-appropriate, source-backed setup for this mcp server profile.

Transport: local_stdio_or_self_hosted_http
Configuration: { "source": "https://github.com/MariaDB/mcp", "requirements": [ "Python 3.11", "uv" ], "env": [ "DB_HOST", "DB_USER", "DB_PASSWORD", "MCP_READ_ONLY=true" ] }
Authentication: MariaDB database credentials; separately configured FastMCP authentication is available when exposing HTTP/SSE transports.
Setup notes: Use the cited setup exactly; secrets/tokens should remain outside prompts and source control. Database access is sensitive. MCP_READ_ONLY is only a best-effort application guard; the project explicitly recommends a dedicated database user with least privileges for true read-only protection. HTTP/SSE deployments require separately configured authentication when exposed beyond localhost.

Read additional publisher installation details

From the publisher README

Publisher installation details

Original client setup and configuration retained from the publisher documentation.

View original README ↗

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 lock
uv sync
```
Create .env in the project root (see Configuration)

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

Loading optional publisher installation details…

Show source-catalog configuration

{"source":"https://github.com/MariaDB/mcp","requirements":["Python 3.11","uv"],"env":["DB_HOST","DB_USER","DB_PASSWORD","MCP_READ_ONLY=true"]}

Read publisher documentation

Publisher documentation

Publisher README

Original product, capability, tool, and usage documentation from the MCP publisher. Installation sections appear under Install & Agents.

View original README ↗

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
Security Considerations
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
`DB_CHARSET`	Character set for database connection (e.g., `cp1251`)	No	MariaDB default
`DB_SSL`	Enable SSL/TLS for database connection (`true`/`false`)	No	`false`
`DB_SSL_CA`	Path to CA certificate file for SSL verification	No
`DB_SSL_CERT`	Path to client certificate file for SSL authentication	No
`DB_SSL_KEY`	Path to client private key file for SSL authentication	No
`DB_SSL_VERIFY_CERT`	Verify server certificate (`true`/`false`)	No	`true`
`DB_SSL_VERIFY_IDENTITY`	Verify server hostname identity (`true`/`false`)	No	`false`
`MCP_READ_ONLY`	Enforce read-only SQL mode (`true`/`false`)	No	`true`
`MCP_BLOCK_SENSITIVE_SHOW`	Block sensitive `SHOW` commands (`PROCESSLIST`, `GRANTS`, `VARIABLES`, `MASTER`/`REPLICA STATUS`, `BINARY LOGS`, etc.) that can leak cross-connection query text, credentials/privileges, or replication topology. Set independently of `MCP_READ_ONLY` (`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

With SSL/TLS Enabled:

DB_HOST=your-remote-host.com
DB_USER=your_db_user
DB_PASSWORD=your_db_password
DB_PORT=3306
DB_NAME=your_default_database

# Enable SSL
DB_SSL=true
DB_SSL_CA=~/.mysql/ca-cert.pem
DB_SSL_CERT=~/.mysql/client-cert.pem
DB_SSL_KEY=~/.mysql/client-key.pem
DB_SSL_VERIFY_CERT=true
DB_SSL_VERIFY_IDENTITY=false

MCP_READ_ONLY=true
MCP_MAX_POOL_SIZE=10

Note on SSL Configuration:

All SSL certificate paths support ~ for home directory expansion
DB_SSL_CA is used to verify the server's certificate
DB_SSL_CERT and DB_SSL_KEY are used for client certificate authentication (mutual TLS)
Set DB_SSL_VERIFY_CERT=false only for testing with self-signed certificates
Set DB_SSL_VERIFY_IDENTITY=true to enable strict hostname verification

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-..."

Database User Privileges - IMPORTANT

⚠️ The only way to guarantee 100% read-only access with absolute certainty is to configure the MariaDB user with appropriate privileges. The READ_ONLY flag is a best effort attempt to prevent write operations, but it is based upon a whitelist of allowed queries and against a truly adversarial user it is not a substitute for proper database user privileges.

For production use, you should create a dedicated database user with minimal privileges. This is also recommended to show the LLM only the data it may need to perform its task even outside of read-only mode.

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

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"
    }
  }
}

Option 4: Docker container

{
  "servers": {
    "mariadb-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-p",
        "9001:9001",
        "-e",
        "DB_HOST=",
        "-e",
        "DB_PORT=",
        "-e",
        "DB_USER=",
        "-e",
        "DB_PASSWORD=",
        "-e",
        "DB_NAME=",
        "mariadb-mcp-server",
        "python",
        "src/server.py",
        "--host",
        "0.0.0.0",
        "--transport",
        "stdio"
      ]
    }
  }
}

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.

Loading optional publisher documentation…

FAQ

What does MariaDB MCP do?

Inspect and query MariaDB databases, with optional vector-search workflows.

Is MariaDB MCP first-party?

Yes. Reviewed publisher/repository evidence establishes a first-party or ecosystem-official implementation.

How do I connect MariaDB MCP?

Use the setup configuration recorded in this reviewed profile and the cited publisher/repository documentation.

What permissions or risks matter for MariaDB MCP?

Database access is sensitive. MCP_READ_ONLY is only a best-effort application guard; the project explicitly recommends a dedicated database user with least privileges for true read-only protection. HTTP/SSE deployments require separately configured authentication when exposed beyond localhost.

Are stars, downloads and context-footprint measurements available?

Implementation-specific public adoption metrics are shown when attributable. Context footprint has not been schema-token measured.

Is this an official MCP?

MariaDB Foundation is listed as First-party MCP in MCP100’s current evidence.

Current official resources:

Publisher documentation ↗

MCP MariaDB Server

Overview
Core Components
Available Tools
Embeddings & Vector Store
Configuration & Environment Variables
Security Considerations
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
`DB_CHARSET`	Character set for database connection (e.g., `cp1251`)	No	MariaDB default
`DB_SSL`	Enable SSL/TLS for database connection (`true`/`false`)	No	`false`
`DB_SSL_CA`	Path to CA certificate file for SSL verification	No
`DB_SSL_CERT`	Path to client certificate file for SSL authentication	No
`DB_SSL_KEY`	Path to client private key file for SSL authentication	No
`DB_SSL_VERIFY_CERT`	Verify server certificate (`true`/`false`)	No	`true`
`DB_SSL_VERIFY_IDENTITY`	Verify server hostname identity (`true`/`false`)	No	`false`
`MCP_READ_ONLY`	Enforce read-only SQL mode (`true`/`false`)	No	`true`
`MCP_BLOCK_SENSITIVE_SHOW`	Block sensitive `SHOW` commands (`PROCESSLIST`, `GRANTS`, `VARIABLES`, `MASTER`/`REPLICA STATUS`, `BINARY LOGS`, etc.) that can leak cross-connection query text, credentials/privileges, or replication topology. Set independently of `MCP_READ_ONLY` (`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`

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

With SSL/TLS Enabled:

DB_HOST=your-remote-host.com
DB_USER=your_db_user
DB_PASSWORD=your_db_password
DB_PORT=3306
DB_NAME=your_default_database

# Enable SSL
DB_SSL=true
DB_SSL_CA=~/.mysql/ca-cert.pem
DB_SSL_CERT=~/.mysql/client-cert.pem
DB_SSL_KEY=~/.mysql/client-key.pem
DB_SSL_VERIFY_CERT=true
DB_SSL_VERIFY_IDENTITY=false

MCP_READ_ONLY=true
MCP_MAX_POOL_SIZE=10

Note on SSL Configuration:

All SSL certificate paths support ~ for home directory expansion
DB_SSL_CA is used to verify the server's certificate
DB_SSL_CERT and DB_SSL_KEY are used for client certificate authentication (mutual TLS)
Set DB_SSL_VERIFY_CERT=false only for testing with self-signed certificates
Set DB_SSL_VERIFY_IDENTITY=true to enable strict hostname verification

# 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-..."

Database User Privileges - IMPORTANT

For production use, you should create a dedicated database user with minimal privileges. This is also recommended to show the LLM only the data it may need to perform its task even outside of read-only mode.

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

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"
    }
  }
}

Option 4: Docker container

{
  "servers": {
    "mariadb-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-p",
        "9001:9001",
        "-e",
        "DB_HOST=",
        "-e",
        "DB_PORT=",
        "-e",
        "DB_USER=",
        "-e",
        "DB_PASSWORD=",
        "-e",
        "DB_NAME=",
        "mariadb-mcp-server",
        "python",
        "src/server.py",
        "--host",
        "0.0.0.0",
        "--transport",
        "stdio"
      ]
    }
  }
}

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.

First-party or well-evidenced implementation identity, meaningful task value, maintained setup evidence, and no material admission veto were found in reviewed sources.

Best for

Not ideal for

Avoid if

Trade-offs

Evidence

Popularity

Maintenance

Trust review

Sources

Install & Agents

Claude Code

Reviewed setup

Publisher installation details

Installation & Setup

Requirements

Steps

What it does

Representative uses

Important limits

Context Footprint

Access & risk

Safety controls

Other options

Google MCP Toolbox for Databases

Redis MCP

Supabase MCP

Publisher README

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

Database User Privileges - IMPORTANT

For production use, you should create a dedicated database user with minimal privileges. This is also recommended to show the LLM only the data it may need to perform its task even outside of read-only mode.

Usage Examples

Standard SQL Query

Create Vector Store

Insert Documents into Vector Store

Semantic Search

Integration - Claude desktop/Cursor/Windsurf/VSCode

Option 1: Direct Command (stdio)

Option 2: SSE Transport

Option 3: HTTP Transport

Option 4: Docker container

Logging

Testing

FAQ

Publisher installation details

Installation & Setup

Requirements

Steps

Publisher README

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

Database User Privileges - IMPORTANT

For production use, you should create a dedicated database user with minimal privileges. This is also recommended to show the LLM only the data it may need to perform its task even outside of read-only mode.

Example `.env` file

Example `.env` file