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Snowflake MCP Service

Snowflake MCP Service

Local
github.com/davidamomDatabases5Updated 57w ago

About Snowflake MCP Service

Snowflake MCP Service is an MCP server for clients that need to query Snowflake databases. It provides SQL execution and CSV export, with managed connections and password or key-pair authentication.

Key features

  1. 01Execute Snowflake SQL queries
  2. 02Export query results to CSV
  3. 03Manage connection lifecycle
  4. 04Reconnect after timeouts
  5. 05Authenticate with password or key pair

Use cases

  1. 01Run SQL from an MCP-compatible client
  2. 02Export query results for downstream use

MCP support

Exposes execute_query and export_to_csv tools to MCP clients. Clients can invoke SQL operations on Snowflake and receive structured results or an exported file path.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Snowflake MCP Service

A Model Context Protocol (MCP) server that provides access to Snowflake databases for any MCP-compatible client.

GitHub repo License: MIT

This server implements the Model Context Protocol to allow any MCP client to:

  • Execute SQL queries on Snowflake databases
  • Automatically handle database connection lifecycle (connect, reconnect on timeout, close)
  • Handle query results and errors
  • Perform database operations safely
  • Connect using either password or key pair authentication

Architecture Overview

What is MCP (Model Context Protocol)?

MCP is a standard protocol that allows applications to communicate with AI models and external services. It enables AI models to access tools and data sources beyond their training data, expanding their capabilities through a standardized communication interface. Key features include:

  • Based on stdio communication (standard input/output)
  • Structured tool definition and discovery
  • Standardized tool call mechanism
  • Structured results transmission
System Components

The Snowflake-MCP server consists of several key components:

  1. MCP Server - Central component that implements the MCP protocol and handles client requests
  2. Snowflake Connection Manager - Manages database connections, including creation, maintenance, and cleanup
  3. Query Processor - Executes SQL queries on Snowflake and processes the results
  4. Authentication Manager - Handles different authentication methods (password or private key)

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Communication Flow

The system works through the following communication flow:

  1. An MCP Client (such as Claude or other MCP-compatible application) sends a request to the MCP Server
  2. The MCP Server authenticates with Snowflake using credentials from the .env file
  3. The MCP Server executes SQL queries on Snowflake
  4. Snowflake returns results to the MCP Server
  5. The MCP Server formats and sends the results back to the MCP Client

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This architecture allows for seamless integration between AI applications and Snowflake databases while maintaining security and efficient connection management.

Installation

  1. Clone this repository
git clone https://github.com/davidamom/snowflake-mcp.git
  1. Install dependencies
pip install -r requirements.txt

Configuration

MCP Client Configuration Example

Below is an example configuration for Claude Desktop, but this server works with any MCP-compatible client. Each client may have its own configuration method:

{
  "mcpServers": {
    "snowflake": {
      "command": "C:\\Users\\YourUsername\\path\\to\\python.exe",
      "args": ["C:\\path\\to\\snowflake-mcp\\server.py"]
    }
  }
}

Configuration parameters:

  • command: Full path to your Python interpreter. Please modify this according to your Python installation location.
  • args: Full path to the server script. Please modify this according to where you cloned the repository.

Example paths for different operating systems:

Windows:

{
  "mcpServers": {
    "snowflake": {
      "command": "C:\\Users\\YourUsername\\anaconda3\\python.exe",
      "args": ["C:\\Path\\To\\snowflake-mcp\\server.py"]
    }
  }
}

MacOS/Linux:

{
  "mcpServers": {
    "snowflake": {
      "command": "/usr/bin/python3",
      "args": ["/path/to/snowflake-mcp/server.py"]
    }
  }
}
Snowflake Configuration

Create a .env file in the project root directory and add the following configuration:

# Snowflake Configuration - Basic Info
SNOWFLAKE_USER=your_username          # Your Snowflake username
SNOWFLAKE_ACCOUNT=YourAccount.Region  # Example: MyOrg.US-WEST-2
SNOWFLAKE_DATABASE=your_database      # Your database
SNOWFLAKE_WAREHOUSE=your_warehouse    # Your warehouse
SNOWFLAKE_ROLE=your_role              # Your role

# Authentication - Choose one method
Authentication Options

This MCP server supports two authentication methods:

  1. Password Authentication

    SNOWFLAKE_PASSWORD=your_password      # Your Snowflake password
    
  2. Key Pair Authentication

    SNOWFLAKE_PRIVATE_KEY_FILE=/path/to/rsa_key.p8     # Path to private key file 
    SNOWFLAKE_PRIVATE_KEY_PASSPHRASE=your_passphrase   # Optional: passphrase if key is encrypted
    

    For key pair authentication, you must first set up key pair authentication with Snowflake:

    • Generate a key pair and register the public key with Snowflake
    • Store the private key file securely on your machine
    • Provide the full path to the private key file in the configuration

    For instructions on setting up key pair authentication, refer to Snowflake documentation on key pair authentication.

If both authentication methods are configured, the server will prioritize key pair authentication.

Connection Management

The server provides automatic connection management features:

  • Automatic connection initialization

    • Creates connection when first query is received
    • Validates connection parameters
  • Connection maintenance

    • Keeps track of connection state
    • Handles connection timeouts
    • Automatically reconnects if connection is lost
  • Connection cleanup

    • Properly closes connections when server stops
    • Releases resources appropriately

Usage

Standard Usage

The server will start automatically when configured with your MCP client. No manual startup is required in normal operation. Once the server is running, your MCP client will be able to execute Snowflake queries.

For development testing, you can start the server manually using:

python server.py

Note: Manual server startup is not needed for normal use. The MCP client will typically manage server startup and shutdown based on the configuration.

Docker Usage

You can also run the server using Docker. This method is recommended for production environments and ensures consistent execution across different platforms.

  1. Build the Docker image:
docker build -t snowflake-mcp .
  1. Configure your MCP client to use Docker. Example configuration:
{
  "mcpServers": {
    "snowflake-docker": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "snowflake-mcp"
      ],
      "env": {
        "SNOWFLAKE_USER": "your_username",
        "SNOWFLAKE_ACCOUNT": "your_account",
        "SNOWFLAKE_DATABASE": "your_database",
        "SNOWFLAKE_WAREHOUSE": "your_warehouse",
        "SNOWFLAKE_PASSWORD": "your_password",
        "SNOWFLAKE_ROLE": "your_role"
        
      }
    }
  }
}

Note: The Docker implementation uses stdio for communication, so no ports need to be exposed.

If using key pair authentication with Docker, you'll need to mount your private key file:

docker run -i -v /path/to/your/key.p8:/app/rsa_key.p8:ro snowflake-mcp

And update your configuration accordingly:

{
  "mcpServers": {
    "Snowflake-Docker": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "-v",
        "/path/to/your/key.p8:/app/rsa_key.p8:ro",
        //optional
        "-v",
        "/path/to/export/dir/:/export/"
        "snowflake-mcp"
      ],
      "env": {
        "SNOWFLAKE_USER": "your_username",
        "SNOWFLAKE_ACCOUNT": "your_account",
        "SNOWFLAKE_DATABASE": "your_database",
        "SNOWFLAKE_WAREHOUSE": "your_warehouse",
        "SNOWFLAKE_ROLE": "your_role",
        "SNOWFLAKE_PRIVATE_KEY_FILE": "path_for_your_private_key",
        "SNOWFLAKE_PRIVATE_KEY_PASSPHRASE": "your_password_for_private_key"
      }
    }
  }
}

Features

  • Secure Snowflake database access
  • Flexible authentication (password or key pair authentication)
  • Robust error handling and reporting
  • Automatic connection management
  • Query execution and result processing
  • Compatible with any MCP-compliant client

Technical Details

Core Components

The implementation consists of several key classes and modules:

  • server.py - The main entry point containing the MCP server implementation.
  • SnowflakeConnection - Class that handles all Snowflake database operations, including:
    • Connection establishment and reconnection
    • Query execution and transaction management
    • Connection maintenance and cleanup
  • SnowflakeMCPServer - The main server class that implements the MCP protocol:
    • Registers available tools with the MCP framework
    • Handles tool call requests from clients
    • Manages the lifecycle of connections
Connection Lifecycle

The connection lifecycle is carefully managed to ensure reliability:

  1. Initialization - Connections are created lazily when the first query is received
  2. Validation - Connection parameters are validated before attempting to connect
  3. Monitoring - Connections are regularly tested for validity
  4. Recovery - Automatic reconnection if the connection is lost or times out
  5. Cleanup - Proper resource release when the server shuts down
MCP Tool Interface

The server exposes the following tool to MCP clients:

  • execute_query - Executes a SQL query on Snowflake and returns the results

    • Input: SQL query string
    • Output: Query results in a structured format
  • export_to_csv - Executes a SQL query on Snowflake and returns the results

    • Input: SQL query string
    • Output: Num rows exported. File path of the output file

This implementation follows best practices for both MCP protocol implementation and Snowflake database interaction.

License

License: MIT

This project is licensed under the MIT License. See the LICENSE file for details.

Copyright (c) 2025 David Amom

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.