Snowflake Data Warehouse MCP

MCP Server

Direct SQL access to Medisolv's Snowflake data warehouse — query tables, explore schemas, describe columns, and sample data without leaving your AI coding environment.

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Medisolv Platform Team

Updated 2026-04-03

v1.0Internal

When to Use

Use this MCP when your agent needs to:

Run SQL queries against Snowflake and inspect results
Explore available databases, schemas, and tables
Describe table columns and data types
Sample rows from a table to understand its structure
Answer data questions like "what columns does the ENCOUNTERS table have?"

Prerequisites

Python 3.11+ installed

If Python is not installed, run this in PowerShell:

winget install Python.Python.3.12 --accept-package-agreements --accept-source-agreements

Upgrade pip and install the Azure Artifacts keyring helpers from public PyPI:

pip install keyring artifacts-keyring

Then install the MCP package locally before configuring Augment. This avoids first-run timeouts caused by downloading packages during MCP startup:

pip install snowflake-mcp --index-url https://pypi.org/simple --extra-index-url "https://medisolv.pkgs.visualstudio.com/ai-discovery-portal/_packaging/ai-discovery/pypi/simple/"

The first install may open an Azure DevOps sign-in flow through artifacts-keyring. Sign in with an account that has access to the ai-discovery feed.

Installation

Configure via mcp.json:

{
  "mcpServers": {
    "snowflake": {
      "command": "python",
      "args": ["-m", "snowflake_mcp.server"],
      "env": {
        "SNOWFLAKE_ACCOUNT": "<SNOWFLAKE_ACCOUNT>",
        "SNOWFLAKE_AUTH_METHOD": "externalbrowser",
        "SNOWFLAKE_USERNAME_SSO": "<SNOWFLAKE_USERNAME_SSO>",
        "SNOWFLAKE_DATABASE": "<SNOWFLAKE_DATABASE>",
        "SNOWFLAKE_WAREHOUSE": "<SNOWFLAKE_WAREHOUSE>",
        "SNOWFLAKE_ROLE": "<SNOWFLAKE_ROLE>"
      }
    }
  }
}

Security Audit

verified_user

Auth

Snowflake Credentials

policy

Scope

Internal Use Only