Which integrations are available for this server?

Provides tools for connecting to and working with MariaDB databases, including query execution, record manipulation, and schema management. Supports MySQL database connections with capabilities for executing queries, managing database schemas, and handling transactions. Enables connection to PostgreSQL databases with support for querying, schema management, and transaction handling through a unified interface. Utilizes SQLAlchemy as the underlying ORM to provide a consistent interface across different database systems. Allows connection to and interaction with SQLite databases, supporting operations like executing queries, managing schemas, and handling transactions.

How do I use Database MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Database MCP Server list all tables in the production database" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Database MCP Server

A Model Context Protocol (MCP) server that provides tools for connecting to and interacting with various database systems.

Features

Multi-Database Support: Connect to SQLite, PostgreSQL, MySQL/MariaDB, and SQL Server databases
Unified Interface: Common tools for database operations across all supported database types
Database-Specific Extensions: Where needed, specific tools for database-specific features
Schema Management: Create, alter, and drop tables and indexes
Query Execution: Execute raw SQL queries or use structured query tools
Transaction Support: Begin, commit, and rollback transactions

Related MCP server: SQLite MCP Server

Installation

Prerequisites

Python 3.8 or higher
Required Python packages (installed automatically with pip):
- SQLAlchemy
- Various database drivers, depending on which databases you want to use:
  - SQLite (included with Python)
  - PostgreSQL: psycopg2-binary
  - MySQL/MariaDB: mysql-connector-python
  - SQL Server: pyodbc

Installing from Source

# Clone the repository
git clone <repository-url>

# Install the package
pip install -e .

Configuration

The server can be configured using environment variables, a configuration file, or by providing connection details at runtime.

Environment Variables

DB_CONFIG_PATH: Path to a JSON configuration file
DB_CONNECTIONS: A comma-separated list of connection IDs or a JSON string with connection details

Configuration File Format

{
  "connections": {
    "sqlite_conn": {
      "type": "sqlite",
      "db_path": "/path/to/database.db"
    },
    "postgres_conn": {
      "type": "postgres",
      "host": "localhost",
      "port": 5432,
      "database": "mydatabase",
      "user": "myuser",
      "password": "mypassword"
    }
  }
}

Usage

Running the Server

As an MCP Server for Claude

# Run with default settings
python -m db_mcp_server

# Specify a configuration file
python -m db_mcp_server --config /path/to/config.json

# Set logging level
python -m db_mcp_server --log-level DEBUG

As a Standalone Web Server (for any LLM)

# Run as a web server
python -m db_mcp_server.web_server

# Specify host and port
python -m db_mcp_server.web_server --host 0.0.0.0 --port 8000

# Specify configuration file and logging level
python -m db_mcp_server.web_server --config /path/to/config.json --log-level DEBUG

Available MCP Tools

Connection Management

add_connection: Add a new database connection
test_connection: Test a database connection
list_connections: List all database connections
remove_connection: Remove a database connection

Query Execution

execute_query: Execute a SQL query
get_records: Get records from a table
insert_record: Insert a record into a table
update_record: Update records in a table
delete_record: Delete records from a table

Schema Management

list_tables: List all tables in a database
get_table_schema: Get the schema for a table
create_table: Create a new table
drop_table: Drop a table
create_index: Create an index on a table
drop_index: Drop an index
alter_table: Alter a table structure

Transaction Management

begin_transaction: Begin a transaction
commit_transaction: Commit a transaction
rollback_transaction: Rollback a transaction

Examples

Add a Connection

{
  "connection_id": "my_sqlite_db",
  "type": "sqlite",
  "db_path": "/path/to/database.db"
}

Execute a Query

{
  "connection_id": "my_sqlite_db",
  "query": "SELECT * FROM users WHERE age > ?",
  "params": [21]
}

Create a Table

{
  "connection_id": "my_sqlite_db",
  "table": "users",
  "columns": [
    {
      "name": "id",
      "type": "INTEGER",
      "primary_key": true,
      "nullable": false
    },
    {
      "name": "name",
      "type": "TEXT",
      "nullable": false
    },
    {
      "name": "email",
      "type": "TEXT",
      "nullable": true
    }
  ]
}

Insert Records

{
  "connection_id": "my_sqlite_db",
  "table": "users",
  "data": {
    "name": "John Doe",
    "email": "john@example.com"
  }
}

Development

Running Tests

# Run all tests
python -m unittest discover

# Run specific test file
python -m unittest tests.test_sqlite

Connecting from Other LLMs

When running as a standalone web server, other LLMs (like Llama 3) can connect to the database MCP server via HTTP. The server exposes the following endpoints:

Endpoints

/list_tools - GET or POST: Returns a list of all available tools with their descriptions and input schemas
/call_tool - POST: Execute a specific database tool

Example: Calling from Another LLM

To use this server with another LLM, have the LLM generate HTTP requests to the server. Here's an example of how you could structure the prompt for an LLM like Llama 3:

You can interact with a database by making HTTP requests to a database service at http://localhost:8000. 
The service provides the following endpoints:

1. To get a list of available tools:
   Make a POST request to: http://localhost:8000/list_tools
   
2. To execute a database tool:
   Make a POST request to: http://localhost:8000/call_tool
   with a JSON body like:
   {
     "name": "tool_name",
     "arguments": {
       "param1": "value1",
       "param2": "value2"
     }
   }

For example, to execute a SQL query, you would make a request like:
POST http://localhost:8000/call_tool
Content-Type: application/json

{
  "name": "execute_query",
  "arguments": {
    "connection_id": "my_db",
    "query": "SELECT * FROM users"
  }
}

Sample Python Code for Client Integration

import requests
import json

# Base URL of the database MCP server
BASE_URL = "http://localhost:8000"

# List available tools
def list_tools():
    response = requests.post(f"{BASE_URL}/list_tools")
    return response.json()

# Execute a database tool
def call_tool(tool_name, arguments):
    payload = {
        "name": tool_name,
        "arguments": arguments
    }
    response = requests.post(f"{BASE_URL}/call_tool", json=payload)
    return response.json()

# Example: List tables in a database
def list_tables(connection_id):
    return call_tool("list_tables", {"connection_id": connection_id})

# Example: Execute a SQL query
def execute_query(connection_id, query, params=None):
    return call_tool("execute_query", {
        "connection_id": connection_id,
        "query": query,
        "params": params
    })

# Example: Add a new connection
def add_connection(connection_id, db_type, **kwargs):
    args = {"connection_id": connection_id, "type": db_type}
    args.update(kwargs)
    return call_tool("add_connection", args)

License

MIT License

Database MCP Server

Database MCP Server

Features

Installation

Prerequisites

Installing from Source

Configuration

Environment Variables

Configuration File Format

Usage

Running the Server

As an MCP Server for Claude

As a Standalone Web Server (for any LLM)

Available MCP Tools

Connection Management

Query Execution

Schema Management

Transaction Management

Examples

Add a Connection

Execute a Query

Create a Table

Insert Records

Development

Running Tests

Connecting from Other LLMs

Endpoints

Example: Calling from Another LLM

Sample Python Code for Client Integration

License

Resources

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