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
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)
- 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.
-
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.
- Executes a read-only SQL query (
-
create_database
- Creates a new database if it doesn't exist.
- Parameters:
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
- 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)
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.
- OpenAI
- Gemini
- Open models from Huggingface
EMBEDDING_PROVIDER
: Set toopenai
,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 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
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):
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) |
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
- Python 3.11 (see
.python-version
) - uv (dependency manager; install instructions)
- MariaDB server (local or remote)
- 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
Adjust entry point if needed (e.g.,
python server.py
main.py
)
{
"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"
}
}
}
or If already running MCP server
{
"servers": {
"mariadb-mcp-server": {
"url": "http://{host}:9001/sse",
"type": "sse"
}
}
}
- 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).
- 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.