py-mcp-mssql MCP Server

What does “py-mcp-mssql” MCP Server do?

The py-mcp-mssql MCP Server is a Python-based implementation of the Model Context Protocol (MCP) designed to provide seamless access to Microsoft SQL Server databases for AI assistants and language models. By exposing database operations over the MCP interface, this server enables AI clients to inspect SQL table schemas, execute queries, and retrieve data in a standardized format. It leverages asynchronous Python capabilities, environment-based configuration, and FastAPI integration for efficient and reliable operation. This facilitates enhanced development workflows for tasks such as data analysis, report generation, and intelligent database management, making it easier for AI models to interact with enterprise-grade SQL databases securely and programmatically.

List of Prompts

No prompt templates are mentioned in the repository or documentation.

List of Resources

• Database Tables Listing
  The server exposes all available tables in the connected MSSQL database as resources, each represented by a URI (e.g., mssql://<table_name>/data).

• Table Data Resource
  Enables reading data from any table by referencing its resource URI, returning the first 100 rows as CSV with column headers.

• Table Descriptions
  When listing resources, table descriptions and MIME types are included for each exposed resource, aiding in context for LLM interactions.

List of Tools

• list_resources
  Lists all available tables in the MSSQL database, returning resource metadata.

• read_resource
  Reads data from a specified table URI, returning up to 100 rows in CSV format.

• SQL Execution
  Supports executing SQL queries via an endpoint, enabling flexible data operations (details referenced but exact tool name not specified).

Use Cases of this MCP Server

• Database Exploration
  AI assistants can list and describe all tables in an MSSQL database, supporting schema discovery and context-building for data science or migration tasks.

• Data Analysis and Visualization
  Enables AI models to fetch tabular data directly from SQL Server for analysis, visualization, or generating reports, streamlining business analytics workflows.

• Automated Report Generation
  By leveraging SQL execution and data retrieval, developers can automate creation of data-driven reports or dashboards using AI.

• Codebase/Data Integration
  Facilitates easy integration of MSSQL data into codebases or other applications via the MCP protocol, supporting ETL and automation pipelines.

• API-driven Database Access
  Offers a secure, standardized API for accessing enterprise SQL data, making it accessible to various AI-powered tools and workflows.

How to set it up

Windsurf

1. Ensure you have Node.js and Python 3.x installed.
2. Install py-mcp-mssql and required dependencies.
3. Locate your Windsurf configuration file (e.g., settings.json).
4. Add the MCP server using the following JSON snippet:

   "mcpServers": {
     "mssql-mcp": {
       "command": "python",
       "args": ["-m", "mssql.server"]
     }
   }
   

5. Save the configuration and restart Windsurf.
6. Verify connection by listing available servers.

Securing API Keys

Store your MSSQL credentials in an .env file:

MSSQL_SERVER=your_server
MSSQL_DATABASE=your_database
MSSQL_USER=your_username
MSSQL_PASSWORD=your_password
MSSQL_DRIVER={ODBC Driver 17 for SQL Server}

Example config with env:

"env": {
  "MSSQL_SERVER": "your_server",
  "MSSQL_DATABASE": "your_db",
  "MSSQL_USER": "your_user",
  "MSSQL_PASSWORD": "your_password",
  "MSSQL_DRIVER": "{ODBC Driver 17 for SQL Server}"
}

Claude

1. Ensure Python 3.x and required packages are installed.
2. Edit the Claude integrations file.
3. Add the MCP server configuration:

   "mcpServers": {
     "mssql-mcp": {
       "command": "python",
       "args": ["-m", "mssql.server"]
     }
   }
   

4. Save and restart Claude.
5. Confirm the MCP server is available.

Cursor

1. Install Python 3.x and all dependencies via pip install -r requirements.txt.
2. Open Cursor’s configuration file.
3. Add the MCP server:

   "mcpServers": {
     "mssql-mcp": {
       "command": "python",
       "args": ["-m", "mssql.server"]
     }
   }
   

4. Save and restart Cursor.
5. Test access to MSSQL resources.

Cline

1. Clone and install the py-mcp-mssql repository.
2. Update your Cline config file.
3. Register the MCP server:

   "mcpServers": {
     "mssql-mcp": {
       "command": "python",
       "args": ["-m", "mssql.server"]
     }
   }
   

4. Save changes and restart Cline.
5. List resources to verify setup.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "mssql-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “mssql-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the available information, py-mcp-mssql is a functional MCP server with clear documentation, standard resource and tool exposure, and good setup instructions, but lacks prompt templates and explicit sampling/Roots support. The overall offering is robust for database use cases but may lack advanced MCP features.

MCP Score