SQL Server
Enables AI assistants to access and query SQL Server databases.
SQL Server MCP Server
A Model Context Protocol (MCP) server that provides AI assistants with SQL Server database access capabilities.
Features
- Execute SQL queries with automatic safety limits
- Database schema inspection and exploration
- Table statistics and performance monitoring
- Advanced search capabilities for tables and columns
- Table backup functionality
- Data insertion with conflict handling
- Query execution plan analysis
- Connection health monitoring
Quick Start
1. Setup Environment
# Navigate to project directory
cd C:\Users\benha\Desktop\sql-server-mcp
# Create virtual environment
python -m venv venv
venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
2. Configure Database Connection
Edit the .env file with your SQL Server credentials:
SQL_SERVER_HOST=192.168.1.117
SQL_SERVER_DATABASE=EM_Data
SQL_SERVER_USERNAME=benhg
SQL_SERVER_PASSWORD=your_actual_password
SQL_SERVER_PORT=1433
3. Test the Server
python -m sql_server_mcp.server
Integration with AI Tools
Claude Desktop
Add to your Claude Desktop configuration file:
Location:
- Windows:
%APPDATA%\Claude\claude_desktop_config.json - macOS:
~/Library/Application Support/Claude/claude_desktop_config.json
Configuration:
{
"mcpServers": {
"sql-server": {
"command": "python",
"args": ["-m", "sql_server_mcp.server"],
"cwd": "C:\\Users\\benha\\Desktop\\sql-server-mcp",
"env": {
"SQL_SERVER_HOST": "192.168.1.117",
"SQL_SERVER_DATABASE": "EM_Data",
"SQL_SERVER_USERNAME": "benhg",
"SQL_SERVER_PASSWORD": "your_password_here",
"SQL_SERVER_PORT": "1433"
}
}
}
}
VS Code
- Install the "Model Context Protocol" extension
- Add to VS Code settings.json:
{
"mcp.servers": {
"sql-server": {
"command": "python",
"args": ["-m", "sql_server_mcp.server"],
"cwd": "C:\\Users\\benha\\Desktop\\sql-server-mcp"
}
}
}
Cursor
Add to Cursor MCP configuration:
{
"mcp": {
"servers": {
"sql-server": {
"command": "python",
"args": ["-m", "sql_server_mcp.server"],
"cwd": "C:\\Users\\benha\\Desktop\\sql-server-mcp"
}
}
}
}
Available Tools
- execute_query - Run SQL queries with safety limits
- get_schema - Inspect database structure
- get_table_info - Detailed table information with samples
- explain_query - Query execution plans
- check_connection - Database connectivity status
- get_table_stats - Table size and performance metrics
- search_tables - Find tables and columns by name
- backup_table - Create table backups
- insert_data - Insert data with conflict handling
Usage Examples
Once integrated with Claude, you can ask:
- "Show me the schema of my SeriesRecord table"
- "Execute: SELECT TOP 10 * FROM SeriesRecord WHERE Source = 'NBP'"
- "What tables do I have in my database?"
- "Create a backup of my SeriesRecord table"
- "Search for any tables containing 'GDP'"
- "Show me statistics for all my tables"
Security Features
- Automatic query limits (TOP 1000 by default)
- Parameterized query support
- Environment-based configuration
- Connection pooling and health checks
- Comprehensive error handling
Development
Running Tests
pip install pytest pytest-asyncio
pytest tests/
Project Structure
sql-server-mcp/
├── sql_server_mcp/
│ ├── __init__.py
│ └── server.py
├── tests/
│ ├── __init__.py
│ └── test_server.py
├── requirements.txt
├── pyproject.toml
├── .env
└── README.md
Troubleshooting
Common Issues
- ODBC Driver Not Found: Install Microsoft ODBC Driver 17 for SQL Server
- Connection Failed: Verify server address, credentials, and network connectivity
- Permission Denied: Ensure database user has appropriate permissions
Testing Connection
# Test ODBC drivers
import pyodbc
print(pyodbc.drivers())
# Test basic connection
from sqlalchemy import create_engine, text
engine = create_engine('mssql+pyodbc://user@server:port/database?driver=ODBC+Driver+17+for+SQL+Server')
with engine.connect() as conn:
result = conn.execute(text('SELECT 1'))
print('Connection successful!')
License
MIT License - see LICENSE file for details.
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