MCP Sqlserver Pro

🚀 MSSQL MCP Server

The MSSQL MCP Server is a Model Context Protocol (MCP) server that offers comprehensive access to Microsoft SQL Server databases. It empowers Language Models to inspect database schemas, execute queries, manage database objects, and perform advanced database operations via a standardized interface.

🚀 Quick Start

To quickly set up the MSSQL MCP Server, follow these steps:

Prerequisites

• Python 3.10 or higher
• ODBC Driver 17 for SQL Server
• Access to an MSSQL Server instance

Quick Setup

1. Clone or create the project directory:

   mkdir mcp-sqlserver && cd mcp-sqlserver
   

2. Run the installation script:

   chmod +x install.sh
   ./install.sh
   

3. Configure your database connection:

   cp env.example .env
   # Edit .env with your database details
   

✨ Features

Complete Database Schema Traversal

• 23 comprehensive database management tools (expanded from 5 basic operations)
• Full database object hierarchy exploration - tables, views, stored procedures, indexes, schemas
• Advanced database object management - create, modify, delete operations
• Intelligent resource access - all tables and views available as MCP resources
• Large content handling - retrieves complete stored procedures (1400+ lines) without truncation

Core Capabilities

• Database Connection: Connect to MSSQL Server instances with flexible authentication
• Schema Inspection: Complete database object exploration and management
• Query Execution: Execute SELECT, INSERT, UPDATE, DELETE, and DDL queries
• Stored Procedure Management: Create, modify, execute, and manage stored procedures
• View Management: Create, modify, delete, and describe views
• Index Management: Create, delete, and analyze indexes
• Resource Access: Browse table and view data as MCP resources
• Security: Read-only and write operations are properly separated and validated

⚠️ Important Note

Database Limitation

🔴 CRITICAL: Limit to ONE database per MCP server instance

• This enhanced MCP server creates 23 tools per database
• Cursor has a 40-tool limit across all MCP servers
• Using multiple database instances will exceed Cursor's tool limit
• For multiple databases, use separate MCP server instances in different projects

Large Content Limitations

⚠️ IMPORTANT: File operations not supported within chat context

• Large stored procedures (1400+ lines) can be retrieved and viewed in chat
• However, saving large content to files via MCP tools is not reliable due to token limits
• For bulk data extraction: Use standalone Python scripts with direct database connections
• Recommended approach: Copy-paste smaller procedures from chat, use external scripts for large ones

Tool Distribution

• Core Tools: 5 (read_query, write_query, list_tables, describe_table, create_table)
• Stored Procedures: 6 tools (create, modify, delete, list, describe, execute, get_parameters)
• Views: 5 tools (create, modify, delete, list, describe)
• Indexes: 4 tools (create, delete, list, describe)
• Schema Management: 2 tools (list_schemas, list_all_objects)
• Total: 23 tools + enhanced write_query supporting all database object operations

📦 Installation

Prerequisites

• Python 3.10 or higher
• ODBC Driver 17 for SQL Server
• Access to an MSSQL Server instance

Quick Setup

1. Clone or create the project directory:

   mkdir mcp-sqlserver && cd mcp-sqlserver
   

2. Run the installation script:

   chmod +x install.sh
   ./install.sh
   

3. Configure your database connection:

   cp env.example .env
   # Edit .env with your database details
   

Manual Installation

1. Create virtual environment:

   python3 -m venv venv
   source venv/bin/activate
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Install ODBC Driver (macOS):

   brew tap microsoft/mssql-release
   brew install msodbcsql17 mssql-tools
   

📚 Documentation

Configuration

Create a .env file with your database configuration:

MSSQL_DRIVER={ODBC Driver 17 for SQL Server}
MSSQL_SERVER=your-server-address
MSSQL_DATABASE=your-database-name
MSSQL_USER=your-username
MSSQL_PASSWORD=your-password
MSSQL_PORT=1433
TrustServerCertificate=yes

Configuration Options

Usage

Understanding MCP Servers

MCP (Model Context Protocol) servers are designed to work with AI assistants and language models. They communicate via stdin/stdout using JSON-RPC protocol, not as traditional web services.

Running the Server

For AI Assistant Integration:

python3 src/server.py

The server will start and wait for MCP protocol messages on stdin. This is how AI assistants like Claude Desktop or other MCP clients will communicate with it.

For Testing and Development:

1. Test database connection:

   python3 test_connection.py
   

2. Check server status:

   ./status.sh
   

3. View available tables:

   # The server provides tools that can be called by MCP clients
   # Direct testing requires an MCP client or testing framework
   

Available Tools (23 Total)

The enhanced server provides comprehensive database management tools:

Core Database Operations (5 tools)

1. read_query - Execute SELECT queries to read data
2. write_query - Execute INSERT, UPDATE, DELETE, and DDL queries
3. list_tables - List all tables in the database
4. describe_table - Get schema information for a specific table
5. create_table - Create new tables

Stored Procedure Management (6 tools)

6. create_procedure - Create new stored procedures
7. modify_procedure - Modify existing stored procedures
8. delete_procedure - Delete stored procedures
9. list_procedures - List all stored procedures with metadata
10. describe_procedure - Get complete procedure definitions
11. execute_procedure - Execute procedures with parameters
12. get_procedure_parameters - Get detailed parameter information

View Management (5 tools)

13. create_view - Create new views
14. modify_view - Modify existing views
15. delete_view - Delete views
16. list_views - List all views in the database
17. describe_view - Get view definitions and schema

Index Management (4 tools)

18. create_index - Create new indexes
19. delete_index - Delete indexes
20. list_indexes - List all indexes (optionally by table)
21. describe_index - Get detailed index information

Schema Exploration (2 tools)

22. list_schemas - List all schemas in the database
23. list_all_objects - List all database objects organized by schema

Available Resources

Both tables and views are exposed as MCP resources with URIs like:

• mssql://table_name/data - Access table data in CSV format
• mssql://view_name/data - Access view data in CSV format

Resources provide the first 100 rows of data in CSV format for quick data exploration.

Database Schema Traversal Examples

1. Explore Database Structure

# Start with schemas
list_schemas

# Get all objects in a specific schema
list_all_objects(schema_name: "dbo")

# Or get all objects across all schemas
list_all_objects()

2. Table Exploration

# List all tables
list_tables

# Get detailed table information
describe_table(table_name: "YourTableName")

# Access table data as MCP resource
# URI: mssql://YourTableName/data

3. View Management

# List all views
list_views

# Get view definition
describe_view(view_name: "YourViewName")

# Create a new view
create_view(view_script: "CREATE VIEW MyView AS SELECT * FROM MyTable WHERE Active = 1")

# Access view data as MCP resource
# URI: mssql://YourViewName/data

4. Stored Procedure Operations

# List all procedures
list_procedures

# Get complete procedure definition (handles large procedures like wmPostPurchase)
describe_procedure(procedure_name: "YourProcedureName")

# Save large procedures to file for analysis
write_file(file_path: "procedure_name.sql", content: "procedure_definition")

# Get parameter details
get_procedure_parameters(procedure_name: "YourProcedureName")

# Execute procedure
execute_procedure(procedure_name: "YourProcedureName", parameters: ["param1", "param2"])

5. Index Management

# List all indexes
list_indexes()

# List indexes for specific table
list_indexes(table_name: "YourTableName")

# Get index details
describe_index(index_name: "IX_YourIndex", table_name: "YourTableName")

# Create new index
create_index(index_script: "CREATE INDEX IX_NewIndex ON MyTable (Column1, Column2)")

Stored Procedure Management Examples

Create a Simple Stored Procedure

CREATE PROCEDURE GetEmployeeCount
AS
BEGIN
    SELECT COUNT(*) AS TotalEmployees FROM Employees
END

Create a Stored Procedure with Parameters

CREATE PROCEDURE GetEmployeesByDepartment
    @DepartmentId INT,
    @MinSalary DECIMAL(10,2) = 0
AS
BEGIN
    SELECT 
        EmployeeId,
        FirstName,
        LastName,
        Salary,
        DepartmentId
    FROM Employees 
    WHERE DepartmentId = @DepartmentId 
    AND Salary >= @MinSalary
    ORDER BY LastName, FirstName
END

Create a Stored Procedure with Output Parameters

CREATE PROCEDURE GetDepartmentStats
    @DepartmentId INT,
    @EmployeeCount INT OUTPUT,
    @AverageSalary DECIMAL(10,2) OUTPUT
AS
BEGIN
    SELECT 
        @EmployeeCount = COUNT(*),
        @AverageSalary = AVG(Salary)
    FROM Employees 
    WHERE DepartmentId = @DepartmentId
END

Modify an Existing Stored Procedure

ALTER PROCEDURE GetEmployeesByDepartment
    @DepartmentId INT,
    @MinSalary DECIMAL(10,2) = 0,
    @MaxSalary DECIMAL(10,2) = 999999.99
AS
BEGIN
    SELECT 
        EmployeeId,
        FirstName,
        LastName,
        Salary,
        DepartmentId,
        HireDate
    FROM Employees 
    WHERE DepartmentId = @DepartmentId 
    AND Salary BETWEEN @MinSalary AND @MaxSalary
    ORDER BY Salary DESC, LastName, FirstName
END

Large Content Handling

How It Works

The server efficiently handles large database objects like stored procedures:

1. Direct Retrieval: Fetches complete content directly from SQL Server
2. No Truncation: Returns full procedure definitions regardless of size
3. Chat Display: Large procedures can be viewed in full within the chat interface
4. Memory Efficient: Processes content through database connection streams

Usage Examples

# Describe a large procedure (gets complete definition)
describe_procedure(procedure_name: "wmPostPurchase")

# Works with procedures of any size (tested with 1400+ line procedures)
# Content is displayed in chat for viewing and copy-paste operations

Limitations for File Operations

⚠️ Important: While large procedures can be retrieved and displayed in chat, saving them to files via MCP tools is not reliable due to inference token limits. For bulk data extraction:

1. Small procedures: Copy-paste from chat interface
2. Large procedures: Use standalone Python scripts with direct database connections
3. Bulk operations: Create dedicated extraction scripts outside the MCP context

Integration with AI Assistants

Claude Desktop

Add this server to your Claude Desktop configuration:

{
  "mcpServers": {
    "mssql": {
      "command": "python3",
      "args": ["/path/to/mcp-sqlserver/src/server.py"],
      "cwd": "/path/to/mcp-sqlserver",
      "env": {
        "MSSQL_SERVER": "your-server",
        "MSSQL_DATABASE": "your-database",
        "MSSQL_USER": "your-username",
        "MSSQL_PASSWORD": "your-password"
      }
    }
  }
}

Other MCP Clients

The server follows the standard MCP protocol and should work with any compliant MCP client.

Development

Project Structure

mcp-sqlserver/
├── src/
│   └── server.py          # Main MCP server implementation with chunking system
├── tests/
│   └── test_server.py     # Unit tests
├── requirements.txt       # Python dependencies
├── .env                   # Database configuration (create from env.example)
├── env.example           # Configuration template
├── install.sh            # Installation script
├── start.sh              # Server startup script (for development)
├── stop.sh               # Server shutdown script
├── status.sh             # Server status script
└── README.md             # This file

Testing

Run the test suite:

python -m pytest tests/

Test database connection:

python3 test_connection.py

Logging

The server uses Python's logging module. Set the log level by modifying the logging.basicConfig() call in src/server.py.

Security Considerations

• Authentication: Always use strong passwords and secure authentication
• Network: Ensure your database server is properly secured
• Permissions: Grant only necessary database permissions to the user account
• SSL/TLS: Use encrypted connections when possible
• Query Validation: The server validates query types and prevents unauthorized operations
• DDL Operations: Create/modify/delete operations for database objects are properly validated
• Stored Procedure Execution: Parameters are safely handled to prevent injection attacks
• Large Content Handling: Large procedures are retrieved efficiently without truncation
• File Operations: Write operations are validated and sandboxed
• Read-First Approach: Exploration tools are read-only by default for production safety

Troubleshooting

Common Issues

1. Connection Failed: Check your database server address, credentials, and network connectivity
2. ODBC Driver Not Found: Install Microsoft ODBC Driver 17 for SQL Server
3. Permission Denied: Ensure the database user has appropriate permissions
4. Port Issues: Verify the correct port number and firewall settings
5. Large Content Issues: Large procedures display in chat but cannot be saved to files via MCP tools
6. Memory Issues: Large content is streamed efficiently from the database

Debug Mode

Enable debug logging by setting the log level to DEBUG in src/server.py:

logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')

Large Content Troubleshooting

If you encounter issues with large content:

1. Copy-paste approach: Use chat interface to view and copy large procedures
2. External scripts: Create standalone Python scripts for bulk data extraction
3. Check memory: Large procedures are handled efficiently by the database connection
4. Verify permissions: Ensure database user can access procedure definitions
5. Test with smaller procedures: Verify basic functionality first

Getting Help

1. Check the server logs for detailed error messages
2. Verify your .env configuration
3. Test the database connection independently
4. Ensure all dependencies are installed correctly
5. For large content issues, use copy-paste from chat or create external extraction scripts

Recent Enhancements

Large Content Handling (Latest)

• Verified complete retrieval of large stored procedures without truncation
• Successfully tested with procedures like wmPostPurchase (1400+ lines, 57KB)
• Large procedures display fully in chat interface for viewing and copy-paste
• Efficient memory handling through database connection streaming
• Note: File operations via MCP tools not reliable for large content due to token limits

Complete Database Object Management

• Expanded from 5 to 23 comprehensive database management tools
• Added full CRUD operations for all major database objects
• Implemented schema traversal capabilities matching SSMS functionality
• Added MCP resource access for tables and views
• Enhanced security with proper operation validation

📄 License

This project is open source. See the license file for details.

💡 Usage Tip

Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests.