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MariaDB/mcp: MariaDB MCP (Model Context Protocol) server implementation

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.

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.
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

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 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")

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 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)

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):

Install dependencies

uv pip compile pyproject.toml -o uv.lock

Create .env in the project root (see Configuration)
Run the server Adjust entry point if needed (e.g., 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"
  }
}

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"}]
  }
}

{
  "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"
    }
  }
}

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.

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