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Snowflake Labs MCP

by Snowflake-Labs

Deprecated and unmaintained; migrate new and existing deployments to Snowflake’s official MCP server.

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Install Snowflake Labs MCP

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "snowflake-labs-mcp": {
      "command": "uvx",
      "args": [
        "snowflake-labs-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

uvx snowflake-labs-mcp

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 19d ago.

Overview

Snowflake-Labs/mcp MCP server previously exposed Snowflake Cortex services, SQL execution, object management, and semantic view tools through MCP. It used the Snowflake Python Connector, honored Snowflake RBAC, and supported configurable stdio, SSE, and streamable HTTP transports. The project is deprecated and no longer maintained, so use Snowflake’s official MCP server for new or existing deployments.

Use cases

  • Query unstructured Snowflake data with Cortex Search
  • Ask questions over structured data through Cortex Analyst
  • Orchestrate structured and unstructured retrieval with Cortex Agents
  • Execute permission-controlled SQL statements
  • Discover and query Snowflake Semantic Views

Key features

  • Cortex Search, Analyst, and Agent tooling
  • Configurable Snowflake object management
  • SQL execution with statement-level permissions
  • Semantic View discovery and querying
  • Snowflake RBAC-aware authentication
  • stdio, SSE, and streamable HTTP transports

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

21/22 checks healthy over the last 79d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 8d ago via OSV.dev · snowflake-labs-mcp (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars303Stargazers on the source repository.
Last commit5mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity3 viewsConfig copies, upvotes, and views on AllMCPs.

How Snowflake Labs MCP works

What Snowflake-Labs/mcp MCP server does

Snowflake-Labs/mcp MCP server was a community implementation for connecting MCP clients to Snowflake workloads. It exposed selected Cortex services and database operations as tools, based on a YAML service configuration loaded when the process started.

The project supported Cortex Search for retrieving unstructured data, Cortex Analyst for querying structured data through semantic models, and Cortex Agents for orchestrating retrieval across structured and unstructured sources. Additional tool groups covered common Snowflake object operations, LLM-generated SQL execution, and discovery or querying of Snowflake Semantic Views.

This repository is deprecated and no longer maintained. Snowflake recommends migrating both new and existing use cases to its official Snowflake MCP Server.

How it works

A configuration file determines which tools become available. Administrators could list Cortex Agent, Search, and Analyst services with their Snowflake locations and descriptions. Separate switches enabled object management, SQL query execution, and semantic view functionality.

SQL-changing operations were governed by an allowlist of statement categories. Administrators could permit or deny operations such as Create, Alter, Insert, Update, Delete, Drop, Select, and transaction commands. Unmapped statement types could also be rejected by leaving the Unknown category disabled.

The server connected through the Snowflake Python Connector and used the role supplied in the connection parameters, or the user’s default role when no role was supplied. Snowflake RBAC therefore remained part of access control alongside the server’s SQL statement permissions.

Setup and configuration

The legacy command supported a YAML configuration file, passed with --service-config-file. A documented local invocation used uvx snowflake-labs-mcp --service-config-file config.yaml with stdio as the default transport.

Connection settings could be supplied as command-line arguments or environment variables. Supported connection options included the account or host, user, password, role, warehouse, MFA passcode, private key, private-key password, authenticator, and a named connection from a Snowflake configuration file. The connector supported username and password, key pair, OAuth, SSO, and MFA authentication methods.

The available transports were stdio for local MCP client connections, legacy SSE for streaming applications, and streamable HTTP for container or remote deployments. The repository cautioned that older account-identifier and personal-access-token options were deprecated in favor of account and password settings.

Tools and capabilities

Snowflake-Labs/mcp MCP server provided configuration-controlled access to:

  • Cortex Search services for unstructured-data retrieval.
  • Cortex Analyst services backed by semantic models or Semantic Views.
  • Cortex Agent services spanning structured and unstructured retrieval.
  • Basic creation, modification, and deletion operations for common Snowflake objects.
  • SQL execution constrained by configured statement permissions.
  • Semantic View discovery and querying.

Search behavior could be driven dynamically by the user’s prompt. When search columns were not specified, the service’s default columns and a limit of 10 were returned.

Limitations and notes

Snowflake-Labs/mcp MCP server should not be selected for a maintained production integration because the repository is explicitly deprecated and unsupported. Its documentation is retained for legacy reference only. The supported migration target is Snowflake’s official MCP Server, which Snowflake continues to develop and support.

The configuration controlled which Cortex services were exposed, so a client could not use services omitted from that file. SQL permissions also required explicit review: enabling broad statement categories could allow tools to change Snowflake objects within the connected role’s privileges.

Read the full README View source on GitHub

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Frequently Asked Questions about Snowflake Labs MCP

Snowflake-Labs/mcp is an MCP server that connects AI clients to Snowflake Cortex services and database operations. Its main tools covered Cortex Search, Cortex Analyst, Cortex Agents, SQL execution, object management, and Semantic View querying. The repository is deprecated and no longer maintained; Snowflake recommends its official MCP Server instead.

Run it with `uvx snowflake-labs-mcp --service-config-file config.yaml` using the documented stdio transport. The repository is deprecated, so Snowflake recommends installing and using the official Snowflake MCP Server for new deployments instead.

Configure the tools in a YAML file passed with `--service-config-file`. List the Cortex Agent, Search, and Analyst services to expose, enable object, query, or semantic managers under `other_services`, and set allowed SQL statement categories under `sql_statement_permissions`.

It authenticates through the Snowflake Python Connector. Supported methods include username and password, key pair, OAuth, SSO, and MFA; connection values can be supplied through CLI arguments or environment variables, and the active Snowflake role determines RBAC access.

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