Сервер Model Context Protocol (MCP), который обеспечивает безопасное взаимодействие с базами данных Microsoft SQL Server. Этот сервер позволяет помощникам ИИ перечислять таблицы, читать данные и выполнять SQL-запросы через контролируемый интерфейс, делая исследование и анализ базы данных более безопасными и структурированными.
Список доступных таблиц SQL Server в качестве ресурсов
Прочитать содержимое таблицы
Выполнять SQL-запросы с правильной обработкой ошибок
Безопасный доступ к базе данных через переменные среды
Комплексное ведение журнала
Автоматическая установка системных зависимостей
Related MCP server: SQL Server MCP
Пакет автоматически установит необходимые системные зависимости (например, FreeTDS) при установке через MCP:
pip install mssql-mcp-serverУстановите следующие переменные среды:
MSSQL_SERVER=localhost
MSSQL_USER=your_username
MSSQL_PASSWORD=your_password
MSSQL_DATABASE=your_databaseДобавьте это в ваш 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
pytestНикогда не фиксируйте переменные среды или учетные данные
Используйте пользователя базы данных с минимальными необходимыми правами.
Рассмотрите возможность внедрения белого списка запросов для использования в производственной среде.
Мониторинг и регистрация всех операций с базой данных
Для работы этого сервера MCP требуется доступ к базе данных. Для безопасности:
Создайте выделенный логин SQL Server с минимальными правами доступа
Никогда не используйте учетные данные sa или административные учетные записи.
Ограничьте доступ к базе данных только необходимыми операциями
Включить ведение журнала для целей аудита
Регулярные проверки безопасности доступа к базе данных
Подробные инструкции см. в Руководстве по настройке безопасности SQL Server :
Создание ограниченного входа в SQL Server
Установка соответствующих разрешений
Мониторинг доступа к базе данных
Лучшие практики безопасности
⚠️ ВАЖНО: Всегда следуйте принципу наименьших привилегий при настройке доступа к базе данных.
Лицензия MIT — подробности см. в файле LICENSE.
Форк репозитория
Создайте ветку функций ( git checkout -b feature/amazing-feature )
Зафиксируйте свои изменения ( git commit -m 'Add some amazing feature' )
Отправить в ветку ( git push origin feature/amazing-feature )
Открыть запрос на извлечение
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.
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