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.

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

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

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

"mcpServers": {
  "simple_snowflake_mcp": {
    "command": "uv",
    "args": [
      "--directory",
      ".", // Use current directory for GitHub
      "run",
      "simple_snowflake_mcp"
    ]
  }
}

"mcpServers": {
  "simple_snowflake_mcp": {
    "command": "uvx",
    "args": [
      "simple_snowflake_mcp"
    ]
  }
}

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

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

Using Docker Compose directly:

# Build the image
docker-compose build
# Start in production mode
docker-compose up -d
# Start in development mode (with volume mounts for live code changes)
docker-compose --profile dev up simple-snowflake-mcp-dev -d
# View logs
docker-compose logs -f
# Stop the service
docker-compose down
# Clean up (remove containers, images, and volumes)
docker-compose down --rmi all --volumes --remove-orphans

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

# See all available commands
make help
# Build and start
make build
make up
# Development mode
make dev-up
# View logs
make logs
# Clean up
make clean

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

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)

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

docker-compose --profile dev up simple-snowflake-mcp-dev -d

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

To prepare the package for distribution:

1. Sync dependencies and update lockfile:

uv sync

2. Build package distributions:

uv build

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

3. Publish to PyPI:

uv publish

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

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:

npx @modelcontextprotocol/inspector uv --directory . run simple-snowflake-mcp

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

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

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:

{
  "name": "execute-snowflake-sql",
  "arguments": { "sql": "SELECT CURRENT_TIMESTAMP;" }
}

The result will be returned in the MCP response.

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

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.