Un servidor de Protocolo de Contexto de Modelo (MCP) que permite la interacción segura con bases de datos de Microsoft SQL Server. Este servidor permite a los asistentes de IA listar tablas, leer datos y ejecutar consultas SQL a través de una interfaz controlada, lo que hace que la exploración y el análisis de bases de datos sean más seguros y estructurados.
Enumerar las tablas de SQL Server disponibles como recursos
Leer el contenido de la tabla
Ejecutar consultas SQL con manejo de errores adecuado
Acceso seguro a la base de datos mediante variables de entorno
Registro completo
Instalación automática de dependencias del sistema
Related MCP server: SQL Server MCP
El paquete instalará automáticamente las dependencias del sistema necesarias (como FreeTDS) cuando se instale a través de MCP:
pip install mssql-mcp-serverEstablezca las siguientes variables de entorno:
MSSQL_SERVER=localhost
MSSQL_USER=your_username
MSSQL_PASSWORD=your_password
MSSQL_DATABASE=your_databaseAgregue esto a su claude_desktop_config.json :
{
"mcpServers": {
"mssql": {
"command": "uv",
"args": [
"--directory",
"path/to/mssql_mcp_server",
"run",
"mssql_mcp_server"
],
"env": {
"MSSQL_SERVER": "localhost",
"MSSQL_USER": "your_username",
"MSSQL_PASSWORD": "your_password",
"MSSQL_DATABASE": "your_database"
}
}
}
}# Install dependencies
pip install -r requirements.txt
# Run the server
python -m mssql_mcp_server# Clone the repository
git clone https://github.com/RichardHan/mssql_mcp_server.git
cd mssql_mcp_server
# Create virtual environment
python -m venv venv
source venv/bin/activate # or `venv\Scripts\activate` on Windows
# Install development dependencies
pip install -r requirements-dev.txt
# Run tests
pytestNunca confirme variables de entorno ni credenciales
Utilice un usuario de base de datos con los permisos mínimos requeridos
Considere implementar la lista blanca de consultas para uso en producción
Supervisar y registrar todas las operaciones de la base de datos
Este servidor MCP requiere acceso a la base de datos para funcionar. Por seguridad:
Cree un inicio de sesión de SQL Server dedicado con permisos mínimos
Nunca utilice credenciales sa ni cuentas administrativas
Restringir el acceso a la base de datos únicamente a las operaciones necesarias
Habilitar el registro para fines de auditoría
Revisiones de seguridad periódicas del acceso a las bases de datos
Consulte la Guía de configuración de seguridad de SQL Server para obtener instrucciones detalladas sobre:
Creación de un inicio de sesión restringido en SQL Server
Establecer permisos apropiados
Monitoreo del acceso a la base de datos
Mejores prácticas de seguridad
⚠️ IMPORTANTE: Siga siempre el principio del mínimo privilegio al configurar el acceso a la base de datos.
Licencia MIT: consulte el archivo LICENCIA para obtener más detalles.
Bifurcar el repositorio
Crea tu rama de funciones ( git checkout -b feature/amazing-feature )
Confirme sus cambios ( git commit -m 'Add some amazing feature' )
Empujar a la rama ( git push origin feature/amazing-feature )
Abrir una solicitud de extracción
Execute an SQL query on the SQL Server
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The SQL query to execute |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description fails to disclose whether queries are read-only or can modify data, potential risks like SQL injection, or result formats. This is a significant gap for a tool that can execute arbitrary SQL.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise at one sentence, but it lacks structure and does not front-load key information beyond the basic action. It could include more detail without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description is incomplete. It does not explain what the tool returns (e.g., result sets, affected rows), error behavior, or any constraints. Critical context for safe usage is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has high coverage (100%) and describes the 'query' parameter sufficiently. The description adds no extra meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'execute' and the resource 'SQL query on the SQL Server', making the purpose straightforward. However, it restates the tool name without adding depth beyond stating it runs queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool or when to avoid it. No mention of prerequisites, security considerations, or alternatives. The description provides no contextual usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0execute_sqlScored across 1 tool
Only one tool exists, so there is no risk of confusion or overlap.
With a single tool, naming is trivially consistent to itself.
A single SQL execution tool is insufficient for the typical scope of SQL Server operations, which usually requires multiple tools for schema exploration, query execution, and data manipulation.
The tool set is severely incomplete; it lacks fundamental operations such as listing tables, describing schemas, or managing databases, limiting useful agent workflows.
Draxlr's remote MCP server connects AI assistants to your SQL databases and dashboards. Explore schemas, run read-only queries, manage saved queries and dashboards, and export results, all with row-level security so each user sees only their own data.
Text-to-SQL MCP server: read-only queries on PostgreSQL, MySQL and SQL Server with your real schema
The BigQuery remote MCP server is a fully managed service that uses the Model Context Protocol to connect AI applications and LLMs to BigQuery data sources. It provides secure, standardized tools for AI agents to list datasets and tables, retrieve schemas, generate and execute SQL queries through natural language, and analyze data—enabling direct access to enterprise analytics data without requiring manual SQL coding.
The HubSpot MCP Server acts as a bridge that enables AI assistants and Large Language Models to securely interact with HubSpot CRM data through natural conversation, without requiring users to understand complex API structures. It provides read-only access to standard CRM objects (contacts, companies, deals, tickets, products, invoices, and more) and their associations, secured via OAuth 2.0, allowing AI agents to perform tasks like summarizing deals, fetching company updates, and looking up record changes.
