SQL Server Agent - Modal Context Protocol
@Amanp17
About SQL Server Agent - Modal Context Protocol
A Model Context Protocol (MCP) implementation enabling communication between an LLM and SQL Server. Allows users to query SQL databases using natural language and get structured SQL responses.
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-sql-server-natural-lang": {
"command": "python",
"args": [
"mcp-ssms-client.py"
]
}
}
}Tools
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Overview
What is SQL Server Agent - Modal Context Protocol?
SQL Server Agent - Modal Context Protocol is a conversational AI Query CLI that enables natural language interaction with SQL Server databases. It acts as a smart layer between large language models and the database, powered by the Modal Context Protocol (MCP). It is designed for developers, analysts, and non-technical users who want to query and manage databases without writing SQL.
How to use SQL Server Agent - Modal Context Protocol?
Ensure you have Python 3.12+ and an OpenAI API key. Clone the repository, install dependencies with pip install -r requirements.txt, and configure a .env file with your OpenAI key and SQL Server credentials. Run python mcp-ssms-client.py and enter your query in plain English when prompted (e.g., “Create an Employee table with 10 dummy records”).
Key features of SQL Server Agent - Modal Context Protocol
- Talk to your database using plain English.
- Perform no-code database operations via natural conversation.
- Execute stored procedures with one-click natural commands.
- Achieve accurate interactions through MCP-enhanced precision.
- Maintain context across multi-step conversations.
Use cases of SQL Server Agent - Modal Context Protocol
- Query your database without writing any SQL.
- Execute stored procedures using conversational commands.
- Perform complex multi-step database tasks with preserved context.
- Enable non-technical users to access and analyse data intuitively.
FAQ from SQL Server Agent - Modal Context Protocol
What is the Modal Context Protocol (MCP)?
MCP is a methodology that defines how to bind context to LLMs. It provides a standardized way to connect AI models to data sources and tools.
Why should I use MCP for database interactions?
MCP simplifies building complex workflows where language models need frequent integration with data sources, making agent development easier and more reliable.
What are the prerequisites for running the server?
You need Python 3.12+ installed on your machine and a valid OpenAI API key. Additionally, you must have SQL Server accessible with credentials and the ODBC Driver 17 for SQL Server.
How do I start interacting with the database?
After setup, run python mcp-ssms-client.py. The script will prompt “Enter your Query:”. Type any request in natural language, like “show me employees and their departments”.
Does the agent remember context across questions?
Yes. The MCP-powered context-aware conversations allow you to ask follow-up questions, and the agent preserves the context from previous interactions.
Frequently asked questions
What is the Modal Context Protocol (MCP)?
MCP is a methodology that defines how to bind context to LLMs. It provides a standardized way to connect AI models to data sources and tools.
Why should I use MCP for database interactions?
MCP simplifies building complex workflows where language models need frequent integration with data sources, making agent development easier and more reliable.
What are the prerequisites for running the server?
You need Python 3.12+ installed on your machine and a valid OpenAI API key. Additionally, you must have SQL Server accessible with credentials and the ODBC Driver 17 for SQL Server.
How do I start interacting with the database?
After setup, run `python mcp-ssms-client.py`. The script will prompt “Enter your Query:”. Type any request in natural language, like “show me employees and their departments”.
Does the agent remember context across questions?
Yes. The MCP-powered context-aware conversations allow you to ask follow-up questions, and the agent preserves the context from previous interactions.
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