SJSINGLE_AI SQL Server MCP

Production-ready Model Context Protocol server for Microsoft SQL Server, configured for the SJSINGLE_AI database.

Features

• SQL Server MCP tools for schema discovery and safe read-only queries

• SJSINGLE_AI configured as the default database

• Optional multi-database catalog through databases.json

• Read-only by default, with DDL/admin commands blocked

• Row limits and query timeout controls

• Live schema metadata discovery

• LangChain-powered natural-language read-only SQL tool

• stdio transport for Cursor and optional streamable HTTP transport

Related MCP server: SQL Server MCP

Setup

cd D:\DHRUVI_MCP_SERVER\DHRUVI_MCP_SERVER
copy .env.example .env

Edit .env and set the real SQL Server password:

MSSQL_SERVER=4.240.84.65
MSSQL_PORT=1433
MSSQL_DATABASE=SJSINGLE_AI
MSSQL_DATABASES=SJSINGLE_AI
MSSQL_USER=sjreadonly
MSSQL_PASSWORD=your-secure-password
MSSQL_MAX_ROWS=10
AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT=your-foundry-model-deployment-name
AZURE_OPENAI_API_KEY=your-azure-openai-key
AZURE_OPENAI_API_VERSION=2024-10-21

Install dependencies:

.\.venv\Scripts\python.exe -m pip install -e ".[dev]"

Run tests:

.\.venv\Scripts\python.exe -m pytest

Start the MCP server:

.\.venv\Scripts\python.exe -m sqlserver_mcp

For HTTP mode, keep these values in .env:

MCP_TRANSPORT=streamable-http
MCP_HTTP_HOST=127.0.0.1
MCP_HTTP_PORT=8765

Then start the same command. The endpoint is:

http://127.0.0.1:8765/mcp

Cursor MCP

Use mcp.json.example as the Cursor MCP configuration template. It points at this folder and starts:

.\.venv\Scripts\python.exe -m sqlserver_mcp

Keep PYTHONPATH=D:\DHRUVI_MCP_SERVER\DHRUVI_MCP_SERVER\src in the MCP environment so Cursor loads the current source files, not an older installed package.

Runtime MCP instructions are passed inline from src/sqlserver_mcp/server.py through FastMCP. AGENT_INSTRUCTIONS.md is kept as human-readable documentation and does not need to be loaded into the MCP runtime.

Main tools:

• list_databases

• list_schemas

• list_tables

• describe_table

• search_objects

• table_column_counts

• recommend_business_view

• get_stone_detail

• execute_query

• execute_parameterized_query

• answer_question_with_langchain

• get_database_info

Safety

The server is read-only unless MSSQL_ALLOW_WRITE=true. Even then, destructive/admin SQL such as DROP, ALTER, CREATE, EXEC, TRUNCATE, BACKUP, and DBCC remains blocked.

LangChain

answer_question_with_langchain uses LangChain to generate one read-only T-SQL query from a natural-language question. The generated query is still executed through the MCP server's normal SQL validator and row cap.

Business questions are routed to preferred MCP views before SQL generation:

• Stone detail, stock, inventory, packet, or diamond questions: MCP.VIEW_STOCK_STONE_DATA

• Party eBid, bid, bidding, auction, or eBid result questions: MCP.VIEW_EBID_RESULT_DETAILS_DATA

• Party, party detail, customer profile, customer expression, interest, or preference questions: MCP.VIEW_CUSTOMER_PROFILE_DATA and MCP.VIEW_CUSTOMER_EXPRESSION_DATA

• Website activity, last activity, user tracking, page visit, or login activity questions: MCP.VIEW_USER_TRACKING_LOG_DATA

Preferred input columns:

• MCP.VIEW_CUSTOMER_EXPRESSION_DATA: PARTY_COMPANY_NAME

• MCP.VIEW_CUSTOMER_PROFILE_DATA: WEB_USER_NAME or COMPANY_NAME

• MCP.VIEW_SALES_STONE_DATA: SERIAL_NO

• MCP.VIEW_STOCK_STONE_DATA: SerialNo or COMPANY_NAME

• MCP.VIEW_USER_TRACKING_LOG_DATA: COMPANY_NAME

• MCP.VIEW_EBID_RESULT_DETAILS_DATA: PARTY_COMPANY_NAME

Query responses are capped at 10 rows.

For a direct stock stone serial lookup, use get_stone_detail(serial_no="533007"). It performs one query against [MCP].[VIEW_STOCK_STONE_DATA] with a SerialNo filter.

Equivalent direct SQL:

SELECT TOP (1) * FROM [MCP].[VIEW_STOCK_STONE_DATA] WHERE [SerialNo] = SerialNo

For more than one serial, use IN and do not add TOP (10):

SELECT * FROM [MCP].[VIEW_STOCK_STONE_DATA] WHERE [SerialNo] IN (SerialNo1, SerialNo2)

For highest stock cost, calculate cost as GRATE * CARAT:

SELECT TOP (1)
    [SERIAL_NO],
    [STONE_ID],
    [GRATE],
    [CARAT],
    ([GRATE] * [CARAT]) AS [CalculatedCost]
FROM [MCP].[VIEW_STOCK_STONE_DATA]
ORDER BY ([GRATE] * [CARAT]) DESC

For highest sale discount, run the direct sales view query once:

SELECT TOP 1 * FROM [MCP].[VIEW_SALES_STONE_DATA] ORDER BY [discount] DESC

Configure:

# Azure AI Foundry / Azure OpenAI
AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT=your-foundry-model-deployment-name
AZURE_OPENAI_API_KEY=your-azure-openai-key
AZURE_OPENAI_API_VERSION=2024-10-21

LANGCHAIN_MODEL=gpt-4o-mini
LANGCHAIN_TEMPERATURE=0
LANGCHAIN_SCHEMA_TABLE_LIMIT=30
LANGCHAIN_INCLUDE_SQL=false

Schema discovery tools read live metadata from SQL Server on each call.