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官方说明10 个来源MCP 服务8 天前
Snowflake-Labs/mcp

Snowflake-Labs/mcp

🐍 ☁️ - Snowflake: prompt Cortex Agents, query structured and unstructured data, manage objects, run SQL and…
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官方说明🐍 ☁️ - Snowflake: prompt Cortex Agents, query structured and unstructured data, manage objects, run SQL and query semantic views, with RBAC and CRUD controls.
配置模板(请按你的客户端调整)
claude mcp add mcp -- npx -y mcp
这是根据仓库名生成的可粘贴骨架,命令行参数与包名请以该项目 README 为准。
★ 数取值
300 ★ · Snowflake-Labs/mcp · 取自该仓库的 GitHub 页面 · (2026-10-16 20:00:20 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 10 个来源列出
评分构成
综合分 1,003 · 300 ★ · 10 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 Snowflake-Labs/mcp;我们收录于 2026-10-09;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

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

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

> [!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 **This project is deprecated and no longer maintained.** Please migrate to the official Snowflake MCP Server.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**: Query unstructured data in Snowflake as commonly used in Retrieval Augmented Generation (RAG) applications.
- **Cortex Analyst**: Query structured data in Snowflake via rich semantic modeling.
- **Cortex Agent**: 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**: 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.
> [!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:
| Parameter | CLI Arguments | Environment Variable | Description |
|-----------|--------------|---------------------|-------------|
| Account | --account | SNOWFLAKE_ACCOUNT | Account identifier (e.g. xy12345.us-east-1) |
| Host | --host | SNOWFLAKE_HOST | Snowflake host URL |
| User | --user, --username | SNOWFLAKE_USER | Username for authentication |
| Password | --password | SNOWFLAKE_PASSWORD | Password or programmatic access token |
| Role | --role | SNOWFLAKE_ROLE | Role to use for connection |
| Warehouse | --warehouse | SNOWFLAKE_WAREHOUSE | Warehouse to use for queries |
| Passcode in Password | --passcode-in-password | - | Whether passcode is embedded in password |
| Passcode | --passcode | SNOWFLAKE_PASSCODE | MFA passcode for authentication |
来源
awesome · https://github.com/Snowflake-Labs/mcp
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