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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.
MariaDB MCP (Model Context Protocol) server implementation
The MCP MariaDB Server exposes a set of tools for interacting with MariaDB databases and vector stores via a standardized protocol. It supports:
.env files.list_databases
list_tables
database_name (string, required)get_table_schema
database_name (string, required), table_name (string, required)get_table_schema_with_relations
database_name (string, required), table_name (string, required)execute_sql
SELECT, SHOW, DESCRIBE).sql_query (string, required), database_name (string, optional), parameters (list, optional)MCP_READ_ONLY is enabled.create_database
database_name (string, required)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
database_name, vector_store_name, model_name (optional), distance_function (optional, default: cosine)delete_vector_store
database_name, vector_store_namelist_vector_stores
database_nameinsert_docs_vector_store
database_name, vector_store_name, documents (list of strings), metadata (optional list of dicts)search_vector_store
database_name, vector_store_name, user_query (string), k (optional, default: 7)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.
EMBEDDING_PROVIDER: Set to openai, gemini, huggingface, or leave unset to disableOPENAI_API_KEY: Required if using OpenAI embeddingsGEMINI_API_KEY: Required if using Gemini embeddingsHF_MODEL: Required if using HuggingFace embeddings (e.g., "intfloat/multilingual-e5-large-instruct" or "BAAI/bge-m3")DEFAULT_OPENAI_MODEL, ALLOWED_OPENAI_MODELS)A vector store table has the following columns:
id: Auto-increment primary keydocument: Text of the documentembedding: VECTOR type (indexed for similarity search)metadata: JSON (optional metadata)All configuration is via environment variables (typically set in a .env file):
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.
.env fileWith 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:
~ for home directory expansionDB_SSL_CA is used to verify the server's certificateDB_SSL_CERT and DB_SSL_KEY are used for client certificate authentication (mutual TLS)DB_SSL_VERIFY_CERT=false only for testing with self-signed certificatesDB_SSL_VERIFY_IDENTITY=true to enable strict hostname verificationExample 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-..."
⚠️ 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.
.python-version)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
{
"tool": "execute_sql",
"parameters": {
"database_name": "test_db",
"sql_query": "SELECT * FROM users WHERE id = %s",
"parameters": [123]
}
}
{
"tool": "create_vector_store",
"parameters": {
"database_name": "test_db",
"vector_store_name": "my_vectors",
"model_name": "text-embedding-3-small",
"distance_function": "cosine"
}
}
{
"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"}]
}
}
{
"tool": "search_vector_store",
"parameters": {
"database_name": "test_db",
"vector_store_name": "my_vectors",
"user_query": "What is the capital of France?",
"k": 5
}
}
{
"mcpServers": {
"MariaDB_Server": {
"command": "uv",
"args": [
"--directory",
"path/to/mariadb-mcp-server/",
"run",
"server.py"
],
"envFile": "path/to/mcp-server-mariadb-vector/.env"
}
}
}
{
"servers": {
"mariadb-mcp-server": {
"url": "http://{host}:9001/sse",
"type": "sse"
}
}
}
{
"servers": {
"mariadb-mcp-server": {
"url": "http://{host}:9001/mcp",
"type": "streamable-http"
}
}
}
{
"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"
]
}
}
}
logs/mcp_server.log by default.config.py and logger setup).src/tests/ directory.src/tests/README.md for an overview.MariaDB MCP (Model Context Protocol) server implementation
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FAQ
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ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
MariaDB ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-05 MariaDB MCP (Model Context Protocol) server implementation
