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OSS Snowflake MCP Server

作者:Snowflake-Labs版本 1.3.5i★ –i在你的电脑上运行
注册名称io.github.Snowflake-Labs/mcp

安装

概览

作者提供的说明(英文原文)

MCP Server for Snowflake from Snowflake Labs

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 数据库与数据集
  • 你需要填写:SNOWFLAKE_PASSWORD, SNOWFLAKE_PRIVATE_KEY

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: SNOWFLAKE_ACCOUNT, SNOWFLAKE_HOST, SNOWFLAKE_USER, SNOWFLAKE_PASSWORD, SNOWFLAKE_ROLE, SNOWFLAKE_WAREHOUSE, SNOWFLAKE_PASSCODE, SNOWFLAKE_PRIVATE_KEY, SNOWFLAKE_PRIVATE_KEY_FILE, SNOWFLAKE_PRIVATE_KEY_FILE_PWD, SNOWFLAKE_MCP_HOST, SNOWFLAKE_MCP_PORT, SNOWFLAKE_MCP_ENDPOINT

标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "snowflake-labs": {
      "command": "uvx",
      "args": [
        "snowflake-labs-mcp",
        "--service-config-file",
        "your-value",
        "--authenticator",
        "snowflake",
        "--transport",
        "stdio",
        "--server-host",
        "0.0.0.0",
        "--port",
        "9000",
        "--endpoint",
        "/mcp"
      ],
      "env": {
        "SNOWFLAKE_ACCOUNT": "your-value",
        "SNOWFLAKE_HOST": "your-value",
        "SNOWFLAKE_USER": "your-value",
        "SNOWFLAKE_PASSWORD": "your-value",
        "SNOWFLAKE_ROLE": "your-value",
        "SNOWFLAKE_WAREHOUSE": "your-value",
        "SNOWFLAKE_PASSCODE": "your-value",
        "SNOWFLAKE_PRIVATE_KEY": "your-value",
        "SNOWFLAKE_PRIVATE_KEY_FILE": "your-value",
        "SNOWFLAKE_PRIVATE_KEY_FILE_PWD": "your-value",
        "SNOWFLAKE_MCP_HOST": "your-value",
        "SNOWFLAKE_MCP_PORT": "your-value",
        "SNOWFLAKE_MCP_ENDPOINT": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add snowflake-labs --env SNOWFLAKE_ACCOUNT=your-value --env SNOWFLAKE_HOST=your-value --env SNOWFLAKE_USER=your-value --env SNOWFLAKE_PASSWORD=your-value --env SNOWFLAKE_ROLE=your-value --env SNOWFLAKE_WAREHOUSE=your-value --env SNOWFLAKE_PASSCODE=your-value --env SNOWFLAKE_PRIVATE_KEY=your-value --env SNOWFLAKE_PRIVATE_KEY_FILE=your-value --env SNOWFLAKE_PRIVATE_KEY_FILE_PWD=your-value --env SNOWFLAKE_MCP_HOST=your-value --env SNOWFLAKE_MCP_PORT=your-value --env SNOWFLAKE_MCP_ENDPOINT=your-value -- uvx snowflake-labs-mcp --service-config-file your-value --authenticator snowflake --transport stdio --server-host 0.0.0.0 --port 9000 --endpoint /mcp

运行前请先填写空着的值。

这个服务器需要在启动命令中填入值,请复制设置后自行填写。

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "snowflake-labs": {
      "command": "uvx",
      "args": [
        "snowflake-labs-mcp",
        "--service-config-file",
        "your-value",
        "--authenticator",
        "snowflake",
        "--transport",
        "stdio",
        "--server-host",
        "0.0.0.0",
        "--port",
        "9000",
        "--endpoint",
        "/mcp"
      ],
      "env": {
        "SNOWFLAKE_ACCOUNT": "your-value",
        "SNOWFLAKE_HOST": "your-value",
        "SNOWFLAKE_USER": "your-value",
        "SNOWFLAKE_PASSWORD": "your-value",
        "SNOWFLAKE_ROLE": "your-value",
        "SNOWFLAKE_WAREHOUSE": "your-value",
        "SNOWFLAKE_PASSCODE": "your-value",
        "SNOWFLAKE_PRIVATE_KEY": "your-value",
        "SNOWFLAKE_PRIVATE_KEY_FILE": "your-value",
        "SNOWFLAKE_PRIVATE_KEY_FILE_PWD": "your-value",
        "SNOWFLAKE_MCP_HOST": "your-value",
        "SNOWFLAKE_MCP_PORT": "your-value",
        "SNOWFLAKE_MCP_ENDPOINT": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器需要在启动命令中填入值,请复制设置后自行填写。

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "snowflake-labs": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "snowflake-labs-mcp",
        "--service-config-file",
        "your-value",
        "--authenticator",
        "snowflake",
        "--transport",
        "stdio",
        "--server-host",
        "0.0.0.0",
        "--port",
        "9000",
        "--endpoint",
        "/mcp"
      ],
      "env": {
        "SNOWFLAKE_ACCOUNT": "your-value",
        "SNOWFLAKE_HOST": "your-value",
        "SNOWFLAKE_USER": "your-value",
        "SNOWFLAKE_PASSWORD": "your-value",
        "SNOWFLAKE_ROLE": "your-value",
        "SNOWFLAKE_WAREHOUSE": "your-value",
        "SNOWFLAKE_PASSCODE": "your-value",
        "SNOWFLAKE_PRIVATE_KEY": "your-value",
        "SNOWFLAKE_PRIVATE_KEY_FILE": "your-value",
        "SNOWFLAKE_PRIVATE_KEY_FILE_PWD": "your-value",
        "SNOWFLAKE_MCP_HOST": "your-value",
        "SNOWFLAKE_MCP_PORT": "your-value",
        "SNOWFLAKE_MCP_ENDPOINT": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • SNOWFLAKE_ACCOUNT env
    Account identifier (e.g. xy12345.us-east-1)
  • SNOWFLAKE_HOST env
    Snowflake host URL
  • SNOWFLAKE_USER env
    Username for authentication
  • SNOWFLAKE_PASSWORD 机密 env
    Password or programmatic access token
  • SNOWFLAKE_ROLE env
    Role to use for connection
  • SNOWFLAKE_WAREHOUSE env
    Warehouse to use for queries
  • SNOWFLAKE_PASSCODE env
    MFA passcode for authentication
  • SNOWFLAKE_PRIVATE_KEY 机密 env
    Private key for key pair authentication
  • SNOWFLAKE_PRIVATE_KEY_FILE env
    Path to private key file
  • SNOWFLAKE_PRIVATE_KEY_FILE_PWD env
    Password for encrypted private key
  • SNOWFLAKE_MCP_HOST env
    Host address to bind the server to (default: 0.0.0.0)
  • SNOWFLAKE_MCP_PORT env
    Port number for the server to listen on (default: 9000)
  • SNOWFLAKE_MCP_ENDPOINT env
    Endpoint path for the MCP server (default: /mcp)

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 Snowflake-Labs 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“数据库与数据集”中的其他服务器比较i

  • 星标数多于此分类中其他 954 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 954 个服务器的 1% 更新
  • 基础检查结果等于或优于此分类中其他 954 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

OSS Snowflake MCP Server 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

你需要填写:SNOWFLAKE_PASSWORD, SNOWFLAKE_PRIVATE_KEY

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(1.3.5)于12个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它?

同一分类中有:Supabase, MongoDB MCP Server, Elasticsearch MCP Server

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Supabase

作者:supabase

MCP server for interacting with the Supabase platform

电脑或云端★ 2,993Apache-2.0昨天更新
安装

MongoDB MCP Server

作者:mongodb-js

MongoDB Model Context Protocol Server

在你的电脑上运行★ 1,157Apache-2.0前天更新
安装

MCP server for interacting with Elasticsearch

在你的电脑上运行★ 315Apache-2.014个月前更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

[DEPRECATED] Snowflake Cortex AI Model Context Protocol (MCP) Server

[!CAUTION]

This project is deprecated and no longer maintained. Please migrate to the official Snowflake MCP Server. The official server is actively developed, fully supported by Snowflake, and continues to add new features.

This community MCP server previously provided tooling for Snowflake Cortex AI, object management, and SQL orchestration. It is no longer supported. For all new and existing use cases, please use the official Snowflake MCP Server instead.


Legacy documentation (for reference only)

The MCP server supported the below capabilities:

  • [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview): Query unstructured data in Snowflake as commonly used in Retrieval Augmented Generation (RAG) applications.
  • [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst): Query structured data in Snowflake via rich semantic modeling.
  • [Cortex Agent](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents): Agentic orchestrator across structured and unstructured data retrieval
  • Object Management: Perform basic operations against Snowflake's most common objects such as creation, dropping, updating, and more.
  • SQL Execution: Run LLM-generated SQL managed by user-configured permissions.
  • [Semantic View Querying](https://docs.snowflake.com/en/user-guide/views-semantic/overview): Discover and query Snowflake Semantic Views

Getting Started

Service Configuration

A simple configuration file is used to drive all tooling. An example can be seen at services/configuration.yaml and a template is below. The path to this configuration file will be passed to the server and the contents used to create MCP server tools at startup.

Cortex Services

Many Cortex Agent, Search, and Analyst services can be added. Ideal descriptions are both highly descriptive and mutually exclusive. Only the explicitly listed Cortex services will be available as tools in the MCP client.

Other Services

Other services include tooling for object management, query execution, and semantic view usage. These groups of tools can be enabled by setting them to True in the other_services section of the configuration file.

SQL Statement Permissions

The sql_statement_permissions section ensures that only approved statements are executed across any tools with access to change Snowflake objects. The list contains SQL expression types. Those marked with True are permitted while those marked with False are not permitted. Please see SQL Execution for examples of each expression type.

agent_services: # List all Cortex Agent services
  • service_name: description: > # Describe contents of the agent service

database_name: schema_name:

  • service_name: description: > # Describe contents of the agent service

database_name: schema_name: search_services: # List all Cortex Search services

  • service_name: description: > # Describe contents of the search service

database_name: schema_name:

  • service_name: description: > # Describe contents of the search service

database_name: schema_name: analyst_services: # List all Cortex Analyst semantic models/views

  • service_name: # Create descriptive name for the service semantic_model: # Fully-qualify semantic YAML model or Semantic View description: > # Describe contents of the analyst service
  • service_name: # Create descriptive name for the service semantic_model: # Fully-qualify semantic YAML model or Semantic View description: > # Describe contents of the analyst service

other_services: # Set desired tool groups to True to enable tools for that group object_manager: True # Perform basic operations against Snowflake's most common objects such as creation, dropping, updating, and more. query_manager: True # Run LLM-generated SQL managed by user-configured permissions. semantic_manager: True # Discover and query Snowflake Semantic Views and their components. sql_statement_permissions: # List SQL statements to explicitly allow (True) or disallow (False).

- All: True # To allow everything, uncomment and set All: True.

  • Alter: True
  • Command: True
  • Comment: True
  • Commit: True
  • Copy: True
  • Create: True
  • Delete: True
  • Describe: True
  • Drop: True
  • Insert: True
  • Merge: True
  • Rollback: True
  • Select: True
  • Transaction: True
  • TruncateTable: True
  • Unknown: False # To allow unknown or unmapped statement types, set Unknown: True.
  • Update: True
  • Use: True

[!NOTE]

Previous versions of the configuration file supported specifying explicit values for columns and limit for each Cortex Search service. Instead, these are now exclusively dynamic based on user prompt. If not specified, a search service's default search_columns will be returned with a limit of 10.

Connecting to Snowflake

The MCP server uses the Snowflake Python Connector for all authentication and connection methods. Please refer to the official Snowflake documentation for comprehensive authentication options and best practices.

The MCP server honors the RBAC permissions assigned to the specified role (as passed in the connection parameters) or default role of the user (if no role is passed to connect).

Connection parameters can be passed as CLI arguments and/or environment variables. The server supports all authentication methods available in the Snowflake Python Connector, including:

  • Username/password authentication
  • Key pair authentication
  • OAuth authentication
  • Single Sign-On (SSO)
  • Multi-factor authentication (MFA)
Connection Parameters

Connection parameters can be passed as CLI arguments and/or environment variables:

ParameterCLI ArgumentsEnvironment VariableDescription
Account--accountSNOWFLAKE_ACCOUNTAccount identifier (e.g. xy12345.us-east-1)
Host--hostSNOWFLAKE_HOSTSnowflake host URL
User--user, --usernameSNOWFLAKE_USERUsername for authentication
Password--passwordSNOWFLAKE_PASSWORDPassword or programmatic access token
Role--roleSNOWFLAKE_ROLERole to use for connection
Warehouse--warehouseSNOWFLAKE_WAREHOUSEWarehouse to use for queries
Passcode in Password--passcode-in-password-Whether passcode is embedded in password
Passcode--passcodeSNOWFLAKE_PASSCODEMFA passcode for authentication
Private Key--private-keySNOWFLAKE_PRIVATE_KEYPrivate key for key pair authentication
Private Key File--private-key-fileSNOWFLAKE_PRIVATE_KEY_FILEPath to private key file
Private Key Password--private-key-file-pwdSNOWFLAKE_PRIVATE_KEY_FILE_PWDPassword for encrypted private key
Authenticator--authenticator-Authentication type (default: snowflake)
Connection Name--connection-name-Name of connection from connections.toml (or config.toml) file

[!WARNING]

Deprecation Notice: The CLI arguments --account-identifier and --pat, as well as the environment variable SNOWFLAKE_PAT, are deprecated and will be removed in a future release. Please use --account and --password (or SNOWFLAKE_ACCOUNT and SNOWFLAKE_PASSWORD) instead.

Transport Configuration

The MCP server supports multiple transport mechanisms. For detailed information about MCP transports, see FastMCP Transport Protocols.

TransportDescriptionUse Case
stdioStandard input/output (default)Local development, MCP client integration
sse (legacy)Server-Sent EventsStreaming applications
streamable-httpStreamable HTTP transportContainer deployments, remote servers

Usage

# Default stdio transport

uvx snowflake-labs-mcp --service-config-file config.yaml

HTTP transport with custom endpoint

uvx snowflake-labs-mcp --service-config-file config.yaml --transport streamable-http --endpoint /my-endpoint

For containers (uses streamable-http on port 9000)

uvx snowflake-labs-mcp --service-config-file config.yaml --transport streamable-http --endpoint /snowflake-mcp

Transport Customizations

Server customizations available for sse and streamable-http transports:

ParameterCLI ArgumentEnvironment VariableDefault

| Host | --server-host | SNOWFLAKE_MCP_HOST | "0.0.0.0" | Port | --port | SNOWFLAKE_MCP_PORT | 9000 | Endpoint | --endpoint | SNOWFLAKE_MCP_ENDPOINT | /mcp | Debug Logging | --verbose | SNOWFLAKE_MCP_VERBOSE | false

Example:

export SNOWFLAKE_MCP_ENDPOINT="/my-mcp"

uvx snowflake-labs-mcp --service-config-file config.yaml --transport streamable-http

Using with MCP Clients

The MCP server is client-agnostic and will work with most MCP Clients that support basic functionality for MCP tools and (optionally) resources. Below are examples for local installation. For connecting to containerized deployments, see Connecting MCP Clients to Containers.

Claude Desktop

To integrate this server with Claude Desktop as the MCP Client, add the following to your app's server configuration. By default, this is located at:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Set the path to the service configuration file and configure your connection method:

{

"mcpServers": { "mcp-server-snowflake": { "command": "uvx", "args": [ "snowflake-labs-mcp", "--service-config-file", " /tools_config.yaml", "--connection-name", "default" ] } } }

Cursor

Register the MCP server in Cursor by opening Cursor and navigating to Settings -> Cursor Settings -> MCP. Add the below:

{

"mcpServers": { "mcp-server-snowflake": { "command": "uvx", "args": [ "snowflake-labs-mcp", "--service-config-file", " /tools_config.yaml", "--connection-name", "default" ] } } }

Add the MCP server as context in the chat.

For troubleshooting Cursor server issues, view the logs by opening the Output panel and selecting Cursor MCP from the dropdown menu.

fast-agent

Update the fastagent.config.yaml mcp server section with the configuration file path and connection name:

# MCP Servers

mcp: servers: mcp-server-snowflake: command: "uvx" args: ["snowflake-labs-mcp", "--service-config-file", " /tools_c

只显示了开头部分;其余内容请在仓库中阅读

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