Snowflake Developer Server

A powerful Model Context Protocol (MCP) server that provides comprehensive Snowflake database operations, Cortex AI services, and data management tools for AI assistants like Claude.

🌟 Features

• 🔧 DDL Operations: Create and manage databases, schemas, tables, and other database objects
• 📊 DML Operations: Insert, update, delete, and query data with full SQL support
• ⚙️ Snowflake Operations: Manage warehouses, grants, roles, and show database objects
• 🔒 Secure Authentication: Support for passwords and Programmatic Access Tokens (PAT)
• 🎯 Simple Connection Pattern: Per-operation connections for reliability and simplicity

🚀 Quick Start

Prerequisites

• Python 3.11+
• UV package manager (install from https://github.com/astral-sh/uv)
• Node.js and npm (for MCP inspector)
• Snowflake account with appropriate permissions
• Snowflake credentials (account identifier, username, password/PAT)

Installation

1. Clone the repository

   git clone https://github.com/mcp-tg/snowflake-developer.git
   cd snowflake-developer
   

2. Set up environment

   # Copy environment template
   cp .env.example .env
   
   # Edit .env with your Snowflake credentials
   # Required: SNOWFLAKE_ACCOUNT, SNOWFLAKE_USER, SNOWFLAKE_PAT (or SNOWFLAKE_PASSWORD)
   

3. Install UV (if not already installed)

   # On macOS/Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # On Windows
   powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
   

🧪 Testing with MCP Inspector

The easiest way to test your setup is using the MCP Inspector:

# Run the development inspector script
./dev-inspector.sh

This will:

• ✅ Create a virtual environment (if needed)
• ✅ Install all dependencies via UV
• ✅ Load your Snowflake credentials from .env
• ✅ Start the MCP Inspector web interface
• ✅ Open your browser to test tools interactively

Note: The script automatically handles UV package installation, so you don't need to manually install dependencies.

First Test: Verify Connection

1. In the Inspector, go to the Tools tab
2. Find test_snowflake_connection and click Run
3. You should see your account details and confirmation that the connection works

🔌 Integration with AI Assistants

Claude Desktop

Option 1: Direct from GitHub (no local clone needed)

{
  "mcpServers": {
    "snowflake-developer": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/mcp-tg/snowflake-developer.git",
        "main.py"
      ],
      "env": {
        "SNOWFLAKE_ACCOUNT": "your-account",
        "SNOWFLAKE_USER": "your-username",
        "SNOWFLAKE_PAT": "your-pat-token"
      }
    }
  }
}

Option 2: Local installation

{
  "mcpServers": {
    "snowflake-developer": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/snowflake-developer",
        "python",
        "main.py"
      ],
      "env": {
        "SNOWFLAKE_ACCOUNT": "your-account",
        "SNOWFLAKE_USER": "your-username",
        "SNOWFLAKE_PAT": "your-pat-token"
      }
    }
  }
}

Setup Instructions:

1. Clone the repository: git clone https://github.com/mcp-tg/snowflake-developer.git
2. Create the Claude Desktop config file: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
3. Add the configuration above, replacing /path/to/snowflake-developer with your actual path
4. Replace credential placeholders with your actual Snowflake credentials
5. Restart Claude Desktop

Cursor

Note: Cursor doesn't support environment variables in MCP configuration. You'll need to use the local installation option or set environment variables globally on your system.

Option 1: Direct from GitHub (requires global env vars)

{
  "mcpServers": {
    "snowflake-developer": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/mcp-tg/snowflake-developer.git",
        "main.py"
      ]
    }
  }
}

Requires setting SNOWFLAKE_ACCOUNT, SNOWFLAKE_USER, and SNOWFLAKE_PAT as system environment variables.

Option 2: Local installation (recommended for Cursor)

{
  "mcpServers": {
    "snowflake-developer": {
      "command": "uv",
      "args": ["run", "/path/to/snowflake-developer/main.py"]
    }
  }
}

Use a local .env file in the project directory with your credentials.

📚 Available Tools (22 Total)

🔧 DDL Tools (8 Tools)

Tools for managing database structure:

Tool	Description	Example in Inspector	Natural Language Query
alter_database	Rename databases	database_name: OLD_DB new_name: NEW_DB	"Rename database OLD_DB to NEW_DB"
alter_schema	Rename or move schemas	schema_name: TEST_DB.OLD_SCHEMA new_name: NEW_SCHEMA	"Rename OLD_SCHEMA to NEW_SCHEMA in TEST_DB"
alter_table	Modify table structure	table_name: TEST_DB.PUBLIC.USERS alter_type: ADD column_name: created_at data_type: TIMESTAMP	"Add a created_at timestamp column to TEST_DB.PUBLIC.USERS table"
create_database	Create a new database	database_name: TEST_DB	"Create a new database called TEST_DB"
create_schema	Create a schema in a database	database_name: TEST_DB schema_name: ANALYTICS	"Create a schema named ANALYTICS in TEST_DB database"
create_table	Create a table with columns	database_name: TEST_DB schema_name: PUBLIC table_name: USERS columns: [{"name": "id", "type": "INT"}, {"name": "email", "type": "VARCHAR(255)"}]	"Create a USERS table in TEST_DB.PUBLIC with id as INT and email as VARCHAR(255)"
drop_database_object	Drop any database object	object_type: TABLE object_name: TEST_DB.PUBLIC.OLD_TABLE	"Drop the table TEST_DB.PUBLIC.OLD_TABLE"
execute_ddl_statement	Run custom DDL SQL	ddl_statement: CREATE VIEW TEST_DB.PUBLIC.ACTIVE_USERS AS SELECT * FROM TEST_DB.PUBLIC.USERS WHERE status = 'active'	"Create a view called ACTIVE_USERS that shows only active users"

📊 DML Tools (6 Tools)

Tools for working with data:

Tool	Description	Example in Inspector	Natural Language Query
delete_data	Delete rows from a table	table_name: TEST_DB.PUBLIC.USERS where_clause: status = 'deleted'	"Delete all users with status 'deleted'"
execute_dml_statement	Run custom DML SQL	dml_statement: UPDATE TEST_DB.PUBLIC.USERS SET last_login = CURRENT_TIMESTAMP() WHERE id = 1	"Update the last login timestamp for user with id 1"
insert_data	Insert rows into a table	table_name: TEST_DB.PUBLIC.USERS data: {"id": 1, "email": "[email protected]", "name": "John Doe"}	"Insert a new user with id 1, email [email protected], and name John Doe into the USERS table"
merge_data	Synchronize data between tables	target_table: TEST_DB.PUBLIC.USERS source_table: TEST_DB.STAGING.NEW_USERS merge_condition: target.id = source.id match_actions: [{"action": "UPDATE", "columns": ["email", "name"], "values": ["source.email", "source.name"]}] not_match_actions: [{"action": "INSERT", "columns": ["id", "email", "name"], "values": ["source.id", "source.email", "source.name"]}]	"Merge new users from staging table into production users table, updating existing records and inserting new ones"
query_data	Query data from tables	table_name: TEST_DB.PUBLIC.USERS columns: ["id", "email", "name"] where_clause: status = 'active' limit: 10	"Show me the first 10 active users with their id, email, and name"
update_data	Update existing rows	table_name: TEST_DB.PUBLIC.USERS data: {"status": "inactive"} where_clause: last_login < '2023-01-01'	"Set status to inactive for all users who haven't logged in since January 2023"

⚙️ Snowflake Operations Tools (8 Tools)

Tools for Snowflake-specific operations:

Tool	Description	Example in Inspector	Natural Language Query
alter_warehouse	Modify warehouse settings	warehouse_name: COMPUTE_WH warehouse_size: MEDIUM auto_suspend: 300	"Change COMPUTE_WH to MEDIUM size and auto-suspend after 5 minutes"
describe_database_object	Get object details	object_name: TEST_DB.PUBLIC.USERS	"Describe the structure of TEST_DB.PUBLIC.USERS table"
execute_sql_query	Run any SQL query	query: SELECT CURRENT_USER(), CURRENT_WAREHOUSE()	"Show me my current user and warehouse"
grant_privileges	Grant permissions	privileges: ["SELECT", "INSERT"] on_type: TABLE on_name: TEST_DB.PUBLIC.USERS to_type: ROLE to_name: ANALYST_ROLE	"Grant SELECT and INSERT on TEST_DB.PUBLIC.USERS table to ANALYST_ROLE"
revoke_privileges	Revoke permissions	privileges: ["SELECT"] on_type: TABLE on_name: TEST_DB.PUBLIC.USERS from_type: ROLE from_name: ANALYST_ROLE	"Revoke SELECT on TEST_DB.PUBLIC.USERS table from ANALYST_ROLE"
set_context	Set database/schema/warehouse/role	context_type: DATABASE context_name: TEST_DB	"Use TEST_DB as the current database"
show_database_objects	List database objects	object_type: DATABASES	"Show me all databases"
test_snowflake_connection	Test connection to Snowflake	(no parameters)	"Test my Snowflake connection"

🏗️ Architecture

The server uses a simple per-operation connection pattern:

• Each tool/resource call creates a fresh Snowflake connection
• Connections are automatically closed after each operation
• No connection pooling or persistence required
• Credentials are read from environment variables

🛡️ Security Best Practices

1. Use Programmatic Access Tokens (PAT) instead of passwords when possible
2. Never commit .env files to version control
3. Use least-privilege roles for your Snowflake user
4. Rotate credentials regularly
5. Consider using external secret management for production

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

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

🆘 Troubleshooting

Connection Issues

• Verify your account identifier format
• Check that your user has appropriate permissions
• Ensure your PAT token hasn't expired
• Test network connectivity to Snowflake

Tool Errors

• Check the error message in the Inspector console
• Verify required parameters are provided
• Ensure database objects exist before referencing them
• Check SQL syntax for custom statements

🚀 FastMCP Framework

This MCP server is built using FastMCP, a modern Python framework that simplifies building Model Context Protocol servers. FastMCP provides:

Why FastMCP?

• 🎯 Simple API: Decorator-based tool and resource registration
• ⚡ High Performance: Async/await support with efficient message handling
• 🔧 Type Safety: Full TypeScript-style type hints and validation
• 📝 Auto Documentation: Automatic tool/resource documentation generation
• 🛡️ Error Handling: Built-in exception handling and response formatting
• 🔌 MCP Compliance: Full compatibility with MCP protocol specification

FastMCP vs Traditional MCP

# Traditional MCP server setup
class MyMCPServer:
    def __init__(self):
        self.tools = {}
    
    def register_tool(self, name, handler, schema):
        # Manual registration and validation
        pass

# FastMCP - Clean and Simple
from fastmcp import FastMCP

mcp = FastMCP("MyServer")

@mcp.tool()
def my_tool(param: str) -> str:
    """Tool with automatic type validation and documentation."""
    return f"Result: {param}"

@mcp.resource("my://resource/{id}")
async def my_resource(id: str, ctx: Context) -> dict:
    """Resource with built-in async support and context."""
    return {"data": f"Resource {id}"}

Key FastMCP Features Used

1. Decorator Registration: Tools are registered using simple decorators
2. Type Validation: Automatic parameter validation using Python type hints
3. Context Management: Built-in context for progress reporting and logging
4. Resource Patterns: URI template matching for dynamic resource endpoints
5. Error Handling: Automatic exception catching and standardized error responses

FastMCP Installation

# Install FastMCP
pip install fastmcp

# Or with UV (recommended)
uv add fastmcp

Learning FastMCP

• Official Docs: FastMCP Documentation
• Examples: Browse FastMCP example servers in the repository
• TypeScript MCP SDK: MCP TypeScript SDK

📚 Additional Resources

Snowflake Resources

• Snowflake Documentation
• Snowflake Python Connector
• Programmatic Access Tokens

MCP Protocol & Tools

• Model Context Protocol Specification
• MCP TypeScript SDK
• MCP Python SDK
• MCP Inspector Tool

Development Tools

• UV Package Manager
• FastMCP Framework
• Claude Desktop Configuration
• Cursor IDE Integration