MCP Server for Snowflake

MCP Server for Snowflake

Enables read-only querying of Snowflake databases through Claude, supporting multiple authentication methods and SQL operations like SELECT, SHOW, DESCRIBE.

Category
访问服务器

README

MCP Server for Snowflake

A Model Context Protocol (MCP) server for performing read-only operations against Snowflake databases. This tool enables Claude to securely query Snowflake data without modifying any information.

Features

  • Flexible authentication to Snowflake using either:
    • Service account authentication with private key
    • External browser authentication for interactive sessions
  • Connection pooling with automatic background refresh to maintain persistent connections
  • Support for querying multiple views and databases in a single session
  • Support for multiple SQL statement types (SELECT, SHOW, DESCRIBE, EXPLAIN, WITH)
  • MCP-compatible handlers for querying Snowflake data
  • Read-only operations with security checks to prevent data modification
  • Support for Python 3.12+
  • Stdio-based MCP server for easy integration with Claude Desktop

Available Tools

The server provides the following tools for querying Snowflake:

  • list_databases: List all accessible Snowflake databases
  • list_views: List all views in a specified database and schema
  • describe_view: Get detailed information about a specific view including columns and SQL definition
  • query_view: Query data from a view with an optional row limit
  • execute_query: Execute custom read-only SQL queries (SELECT, SHOW, DESCRIBE, EXPLAIN, WITH) with results formatted as markdown tables

Installation

Prerequisites

  • Python 3.12 or higher
  • A Snowflake account with either:
    • A configured service account (username + private key), or
    • A regular user account for browser-based authentication
  • uv package manager (recommended)

Steps

  1. Clone this repository:

    git clone https://github.com/yourusername/snowflake-mcp-server.git
    cd snowflake-mcp-server
    
  2. Install the package:

    uv pip install -e .
    
  3. Create a .env file with your Snowflake credentials:

    Choose one of the provided example files based on your preferred authentication method:

    For private key authentication:

    cp .env.private_key.example .env
    

    Then edit the .env file to set your Snowflake account details and path to your private key.

    For external browser authentication:

    cp .env.browser.example .env
    

    Then edit the .env file to set your Snowflake account details.

Usage

Running with uv

After installing the package, you can run the server directly with:

uv run snowflake-mcp

# Or you can be explicit about using stdio transport
uv run snowflake-mcp-stdio

This will start the stdio-based MCP server, which can be connected to Claude Desktop or any MCP client that supports stdio communication.

When using external browser authentication, a browser window will automatically open prompting you to log in to your Snowflake account.

Claude Desktop Integration

  1. In Claude Desktop, go to Settings → MCP Servers

  2. Add a new server with the full path to your uv executable:

    "snowflake-mcp-server": {
       "command": "uv",
       "args": [
          "--directory",
          "/<path-to-code>/snowflake-mcp-server",
          "run",
          "snowflake-mcp"
       ]
    }
    

    Or explicitly specify the stdio transport:

    "snowflake-mcp-server": {
       "command": "uv",
       "args": [
          "--directory",
          "/<path-to-code>/snowflake-mcp-server",
          "run",
          "snowflake-mcp-stdio"
       ]
    }
    
  3. You can find your uv path by running which uv in your terminal

  4. Save the server configuration

Example Queries

When using with Claude, you can ask questions like:

  • "Can you list all the databases in my Snowflake account?"
  • "List all views in the MARKETING database"
  • "Describe the structure of the CUSTOMER_ANALYTICS view in the SALES database"
  • "Show me sample data from the REVENUE_BY_REGION view in the FINANCE database"
  • "Run this SQL query: SELECT customer_id, SUM(order_total) as total_spend FROM SALES.ORDERS GROUP BY customer_id ORDER BY total_spend DESC LIMIT 10"
  • "Query the MARKETING database to find the top 5 performing campaigns by conversion rate"
  • "Compare data from views in different databases by querying SALES.CUSTOMER_METRICS and MARKETING.CAMPAIGN_RESULTS"

Configuration

Connection pooling behavior can be configured through environment variables:

  • SNOWFLAKE_CONN_REFRESH_HOURS: Time interval in hours between connection refreshes (default: 8)

Example .env configuration:

# Set connection to refresh every 4 hours
SNOWFLAKE_CONN_REFRESH_HOURS=4

Authentication Methods

Private Key Authentication

This method uses a service account and private key for non-interactive authentication, ideal for automated processes.

  1. Create a key pair for your Snowflake user following Snowflake documentation
  2. Set SNOWFLAKE_AUTH_TYPE=private_key in your .env file
  3. Provide the path to your private key in SNOWFLAKE_PRIVATE_KEY_PATH

External Browser Authentication

This method opens a browser window for interactive authentication.

  1. Set SNOWFLAKE_AUTH_TYPE=external_browser in your .env file
  2. When you start the server, a browser window will open asking you to log in
  3. After authentication, the session will remain active for the duration specified by your Snowflake account settings

Security Considerations

This server:

  • Enforces read-only operations (only SELECT, SHOW, DESCRIBE, EXPLAIN, and WITH statements are allowed)
  • Automatically adds LIMIT clauses to prevent large result sets
  • Uses secure authentication methods for connections to Snowflake
  • Validates inputs to prevent SQL injection

⚠️ Important: Keep your .env file secure and never commit it to version control. The .gitignore file is configured to exclude it.

Development

Static Type Checking

mypy mcp_server_snowflake/

Linting

ruff check .

Formatting

ruff format .

Running Tests

pytest

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Technical Details

This project uses:

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选