SJSINGLE_AI SQL Server MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@SJSINGLE_AI SQL Server MCPwhat are the top 10 most expensive stones?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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_AIconfigured as the default databaseOptional multi-database catalog through
databases.jsonRead-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: mssql-mcp-server-python
Setup
cd D:\DHRUVI_MCP_SERVER\DHRUVI_MCP_SERVER
copy .env.example .envEdit .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=2024-10-21Install dependencies:
.\.venv\Scripts\python.exe -m pip install -e ".[dev]"Run tests:
.\.venv\Scripts\python.exe -m pytestStart the MCP server:
.\.venv\Scripts\python.exe -m sqlserver_mcpFor HTTP mode, keep these values in .env:
MCP_TRANSPORT=streamable-http
MCP_HTTP_HOST=127.0.0.1
MCP_HTTP_PORT=8765Then start the same command. The endpoint is:
http://127.0.0.1:8765/mcpCursor MCP
Use mcp.json.example as the Cursor MCP configuration template. It points at this folder and starts:
.\.venv\Scripts\python.exe -m sqlserver_mcpKeep 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_databaseslist_schemaslist_tablesdescribe_tablesearch_objectstable_column_countsrecommend_business_view
get_stone_detailexecute_queryexecute_parameterized_queryanswer_question_with_langchainget_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_DATAParty eBid, bid, bidding, auction, or eBid result questions:
MCP.VIEW_EBID_RESULT_DETAILS_DATAParty, party detail, customer profile, customer expression, interest, or preference questions:
MCP.VIEW_CUSTOMER_PROFILE_DATAandMCP.VIEW_CUSTOMER_EXPRESSION_DATAWebsite 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_NAMEMCP.VIEW_CUSTOMER_PROFILE_DATA:WEB_USER_NAMEorCOMPANY_NAMEMCP.VIEW_SALES_STONE_DATA:SERIAL_NOMCP.VIEW_STOCK_STONE_DATA:SerialNoorCOMPANY_NAMEMCP.VIEW_USER_TRACKING_LOG_DATA:COMPANY_NAMEMCP.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] = SerialNoFor 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]) DESCFor highest sale discount, run the direct sales view query once:
SELECT TOP 1 * FROM [MCP].[VIEW_SALES_STONE_DATA] ORDER BY [discount] DESCConfigure:
# 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=falseSchema discovery tools read live metadata from SQL Server on each call.
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