Official description๐ โ๏ธ - Snowflake: prompt Cortex Agents, query structured and unstructured data, manage objects, run SQL and query semantic views, with RBAC and CRUD controls.
Config template (adjust for your client)
claude mcp add mcp -- npx -y mcp
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
Star count
300 โ ยท Snowflake-Labs/mcp ยท read from the repository page ยท (checked 2026-10-16 20:00:20)
Source
collected from the punkpeye/awesome-mcp-servers list ยท type: MCP server
Harvest history
first seen 2026-10-09 ยท last seen 2026-10-16 ยท seen 32ร ยท listed by 10 sources
Score
score 1,003 ยท 300 โ ยท 10 sources ยท mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP ๆๅก. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository Snowflake-Labs/mcp๏ผfirst catalogued 2026-10-09๏ผlast seen 2026-10-16ใThere is no official certification or quality rating here โ we record sources and dates only.
Third-party quality signals
[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 **This project is deprecated and no longer maintained.** Please migrate to the official Snowflake MCP Server.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**: 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 |