Simple Snowflake MCP

Simple Snowflake MCP server

Simple Snowflake MCP Server to work behind a corporate proxy (because I could not get that in a few minutes with existing servers, but my own server, yup). Still don't know if it's good or not. But it's good enough for now.

Tools

The server exposes the following MCP tools to interact with Snowflake:

• execute-snowflake-sql: Executes a SQL query on Snowflake and returns the result (list of dictionaries)
• list-snowflake-warehouses: Lists available Data Warehouses (DWH) on Snowflake
• list-databases: Lists all accessible Snowflake databases
• list-views: Lists all views in a database and schema
• describe-view: Gives details of a view (columns, SQL)
• query-view: Queries a view with an optional row limit (markdown result)
• execute-query: Executes a SQL query in read-only mode (SELECT, SHOW, DESCRIBE, EXPLAIN, WITH) or not (if read_only is false), result in markdown format

Quickstart

Install

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json

On Windows: %APPDATA%/Claude/claude_desktop_config.json

Docker Setup

Prerequisites

• Docker and Docker Compose installed on your system
• Your Snowflake credentials

Quick Start with Docker

1. Clone the repository
   git clone <your-repo> cd simple_snowflake_mcp
2. Set up environment variables
   cp .env.example .env # Edit .env with your Snowflake credentials
3. Build and run with Docker Compose
   # Build the Docker image docker-compose build # Start the service docker-compose up -d # View logs docker-compose logs -f

Docker Commands

Using Docker Compose directly:

Using the provided Makefile (Windows users can use make with WSL or install make for Windows):

Docker Configuration

The Docker setup includes:

• Dockerfile: Multi-stage build with Python 3.11 slim base image
• docker-compose.yml: Service definition with environment variable support
• .dockerignore: Optimized build context
• Makefile: Convenient commands for Docker operations

Environment Variables

All Snowflake configuration can be set via environment variables:

• SNOWFLAKE_USER: Your Snowflake username (required)
• SNOWFLAKE_PASSWORD: Your Snowflake password (required)
• SNOWFLAKE_ACCOUNT: Your Snowflake account identifier (required)
• SNOWFLAKE_WAREHOUSE: Warehouse name (optional)
• SNOWFLAKE_DATABASE: Default database (optional)
• SNOWFLAKE_SCHEMA: Default schema (optional)
• MCP_READ_ONLY: Set to "TRUE" for read-only mode (default: TRUE)

Development Mode

For development, use the development profile which mounts your source code:

This allows you to make changes to the code without rebuilding the Docker image.

Development

Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:

2. Build package distributions:

This will create source and wheel distributions in the dist/ directory.

3. Publish to PyPI:

Note: You'll need to set PyPI credentials via environment variables or command flags:

• Token: --token or UV_PUBLISH_TOKEN
• Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

New Feature: Snowflake SQL Execution

The server exposes an MCP tool execute-snowflake-sql to execute a SQL query on Snowflake and return the result.

Usage

Call the MCP tool execute-snowflake-sql with a sql argument containing the SQL query to execute. The result will be returned as a list of dictionaries (one per row).

Example:

The result will be returned in the MCP response.

Installation and configuration in VS Code

1. Clone the project and install dependencies
   git clone <your-repo> cd simple_snowflake_mcp python -m venv .venv .venv/Scripts/activate # Windows pip install -r requirements.txt # or `uv sync --dev --all-extras` if available
2. Configure Snowflake access
   • Copy .env.example to .env (or create .env at the root) and fill in your credentials:
     SNOWFLAKE_USER=... SNOWFLAKE_PASSWORD=... SNOWFLAKE_ACCOUNT=... # SNOWFLAKE_WAREHOUSE Optional: Snowflake warehouse name # SNOWFLAKE_DATABASE Optional: default database name # SNOWFLAKE_SCHEMA Optional: default schema name # MCP_READ_ONLY=true|false Optional: true/false to force read-only mode
3. Configure VS Code for MCP debugging
   • The .vscode/mcp.json file is already present:
     { "servers": { "simple-snowflake-mcp": { "type": "stdio", "command": ".venv/Scripts/python.exe", "args": ["-m", "simple_snowflake_mcp"] } } }
   • Open the command palette (Ctrl+Shift+P), type MCP: Start Server and select simple-snowflake-mcp.
4. Usage
   • The exposed MCP tools allow you to query Snowflake (list-databases, list-views, describe-view, query-view, execute-query, etc.).
   • For more examples, see the MCP protocol documentation: https://github.com/modelcontextprotocol/create-python-server

Supported MCP Functions

The server exposes the following MCP tools to interact with Snowflake:

• execute-snowflake-sql: Executes a SQL query on Snowflake and returns the result (list of dictionaries)
• list-snowflake-warehouses: Lists available Data Warehouses (DWH) on Snowflake
• list-databases: Lists all accessible Snowflake databases
• list-views: Lists all views in a database and schema
• describe-view: Gives details of a view (columns, SQL)
• query-view: Queries a view with an optional row limit (markdown result)
• execute-query: Executes a SQL query in read-only mode (SELECT, SHOW, DESCRIBE, EXPLAIN, WITH) or not (if read_only is false), result in markdown format

For each tool, see the Usage section or the MCP documentation for the call format.