Snowflake MCP Server 🎿

A Model Context Protocol (MCP) server that enables Claude to interact with your Snowflake data warehouse through natural language.

🌟 What is this?

Instead of logging into Snowflake UI and writing SQL manually, simply ask Claude in natural language and let it query your data warehouse for you!

This MCP server gives Claude the ability to:

• ✅ Execute SQL queries on Snowflake
• ✅ List tables in your schemas
• ✅ Describe table structures
• ✅ Check data freshness (when tables were last updated)

How It's Different

Traditional Approach	With Snowflake MCP
Open Snowflake UI → Write SQL → Run → Copy results	Ask Claude → Get insights instantly
Context switching between tools	Everything in one conversation
Remember exact table/column names	Claude helps you discover schema
Repetitive daily checks	Automate monitoring workflows

📋 Table of Contents

• Quick Start
• Installation
• Configuration
• Available Tools
• Usage Examples
• How It Works
• Security
• Extending
• Troubleshooting
• FAQ
• Contributing
• License

🚀 Quick Start

See QUICKSTART.md for a 5-minute setup guide!

# 1. Clone the repo
git clone https://github.com/Legolasan/snowflake_mcp.git
cd snowflake_mcp

# 2. Install dependencies
pip install -r requirements.txt

# 3. Configure credentials
cp .env.example .env
nano .env  # Add your Snowflake credentials

# 4. Test connection
python test_connection.py

# 5. Add to Claude Code (see Configuration section)

📦 Installation

Prerequisites

• Python 3.8 or higher
• Snowflake account with access credentials
• Claude Code CLI (for MCP integration) - Get it here

Note: You do NOT need a separate Claude API key! The MCP server runs locally and only requires Snowflake credentials. See FAQ for details.

Install Dependencies

pip install -r requirements.txt

This installs:

• mcp - Model Context Protocol SDK
• snowflake-connector-python - Snowflake database driver
• python-dotenv - Environment variable management

⚙️ Configuration

1. Snowflake Credentials

Copy the example environment file:

cp .env.example .env

Edit .env with your Snowflake credentials:

SNOWFLAKE_USER=your_username
SNOWFLAKE_PASSWORD=your_password
SNOWFLAKE_ACCOUNT=abc12345.us-east-1
SNOWFLAKE_WAREHOUSE=COMPUTE_WH
SNOWFLAKE_DATABASE=PROD_DB
SNOWFLAKE_SCHEMA=PUBLIC

Finding your account identifier:

• Your Snowflake URL: https://abc12345.us-east-1.snowflakecomputing.com
• Your account ID: abc12345.us-east-1

⚠️ Security: Never commit .env to git! It's protected by .gitignore.

2. Claude Code MCP Configuration

Add this to your Claude Code MCP settings:

File: ~/.config/claude-code/mcp.json

Option A: Using .env file (Recommended)

{
  "mcpServers": {
    "snowflake": {
      "command": "bash",
      "args": [
        "-c",
        "source /path/to/snowflake_mcp/.env && python /path/to/snowflake_mcp/server.py"
      ]
    }
  }
}

Option B: Direct environment variables

{
  "mcpServers": {
    "snowflake": {
      "command": "python",
      "args": ["/path/to/snowflake_mcp/server.py"],
      "env": {
        "SNOWFLAKE_USER": "your_username",
        "SNOWFLAKE_PASSWORD": "your_password",
        "SNOWFLAKE_ACCOUNT": "your_account",
        "SNOWFLAKE_WAREHOUSE": "COMPUTE_WH",
        "SNOWFLAKE_DATABASE": "your_database",
        "SNOWFLAKE_SCHEMA": "PUBLIC"
      }
    }
  }
}

3. Restart Claude Code

After updating the MCP configuration, restart Claude Code to load the server.

🛠️ Available Tools

1. query_snowflake

Execute any SQL query on Snowflake and get formatted results.

Parameters:

• sql (required): SQL query to execute
• limit (optional): Max rows to return (default: 100)

Example prompts:

• "Show me the top 10 customers by revenue"
• "How many orders were placed yesterday?"
• "What's the average order value this month?"

2. list_tables

List all tables in a schema with row counts.

Parameters:

• schema (optional): Schema name (defaults to configured schema)

Example prompts:

• "What tables are available?"
• "List all tables in the ANALYTICS schema"

3. describe_table

Get detailed structure of a table (columns, types, constraints).

Parameters:

• table_name (required): Name of the table

Example prompts:

• "Describe the ORDERS table"
• "What columns are in the CUSTOMERS table?"
• "Show me the schema for user_events"

4. check_table_freshness

Monitor when data was last updated (requires timestamp column).

Parameters:

• table_name (required): Table to check
• timestamp_column (optional): Timestamp column name (default: UPDATED_AT)

Example prompts:

• "When was the ORDERS table last updated?"
• "Is my ETL pipeline running? Check staging table freshness"
• "Show me data recency for all my tables"

💡 Usage Examples

Basic Queries

You: "What tables do we have in Snowflake?"
Claude: [uses list_tables]
        "You have 12 tables including ORDERS, CUSTOMERS, PRODUCTS..."

You: "Show me the structure of the orders table"
Claude: [uses describe_table]
        "The ORDERS table has 8 columns: ORDER_ID (NUMBER),
         CUSTOMER_ID (NUMBER), ORDER_DATE (TIMESTAMP)..."

You: "Get the top 5 orders from today"
Claude: [uses query_snowflake with SQL]
        "Here are the top 5 orders from today..."

Data Monitoring

You: "Is my data fresh? Check when orders table was last updated"
Claude: [uses check_table_freshness]
        "Orders table was last updated 2 hours ago with 1,234 total rows"

You: "Are there any gaps in yesterday's data?"
Claude: [writes and executes SQL to check]
        "I found a 3-hour gap between 2am-5am..."

Multi-Step Analysis

You: "Compare this week's sales to last week"
Claude: [executes multiple queries]
        "This week: $125K (up 15% from last week's $108K)..."

You: "Show me the breakdown by category"
Claude: [refines query based on context]
        "Electronics: +25%, Clothing: +10%, Home: -5%..."

🔍 How It Works

┌─────────────┐         ┌──────────────┐         ┌─────────────┐
│   You ask   │────────▶│  Claude Code │────────▶│ MCP Server  │
│  Claude in  │         │  decides      │         │  executes   │
│ natural     │         │  which tool   │         │  on         │
│ language    │         │  to use       │         │  Snowflake  │
└─────────────┘         └──────────────┘         └─────────────┘
                                                         │
                                                         ▼
┌─────────────┐         ┌──────────────┐         ┌─────────────┐
│  Claude     │◀────────│  Results     │◀────────│  Snowflake  │
│  analyzes & │         │  returned    │         │  Database   │
│  explains   │         │              │         │             │
└─────────────┘         └──────────────┘         └─────────────┘

1. You ask Claude a question about your data
2. Claude decides which tool to use (or writes custom SQL)
3. MCP server executes the query on Snowflake
4. Results are returned to Claude
5. Claude analyzes and responds with insights!

🔒 Security

Best Practices

• ✅ Use read-only credentials for the MCP server
• ✅ Never commit .env file to version control
• ✅ Use dedicated warehouse for MCP queries
• ✅ Set resource monitors to control costs
• ✅ Review queries before execution (Claude shows them to you)
• ✅ Limit result sizes (default 100 rows, configurable)

Snowflake Permissions

Recommended minimal permissions:

-- Create a read-only role for MCP
CREATE ROLE MCP_READONLY;

-- Grant database and schema access
GRANT USAGE ON DATABASE PROD_DB TO ROLE MCP_READONLY;
GRANT USAGE ON SCHEMA PROD_DB.PUBLIC TO ROLE MCP_READONLY;

-- Grant SELECT on all tables
GRANT SELECT ON ALL TABLES IN SCHEMA PROD_DB.PUBLIC TO ROLE MCP_READONLY;

-- Grant warehouse usage
GRANT USAGE ON WAREHOUSE COMPUTE_WH TO ROLE MCP_READONLY;

-- Assign role to user
GRANT ROLE MCP_READONLY TO USER mcp_user;

🚀 Extending This Server

Want to add more capabilities? Here's how:

Adding a New Tool

1. Add the tool function in server.py:

@self.app.call_tool()
async def check_data_quality(arguments: dict[str, Any]) -> list[TextContent]:
    """Check for nulls, duplicates, and anomalies"""
    table_name = arguments.get("table_name")

    # Your implementation here
    conn = self._get_connection()
    cursor = conn.cursor()
    # ... run quality checks

    return [TextContent(type="text", text=results)]

2. Register the tool in the tools property:

Tool(
    name="check_data_quality",
    description="Check table for data quality issues",
    inputSchema={
        "type": "object",
        "properties": {
            "table_name": {"type": "string", "description": "Table to check"}
        },
        "required": ["table_name"]
    }
)

3. Restart the MCP server and Claude will have access to it!

Ideas for New Tools

• 📊 Data Quality Checks - Null counts, duplicates, outliers
• 💰 Cost Monitoring - Query costs, warehouse usage, credit burn
• ⚡ Performance Analysis - Slow queries, table statistics
• 🔄 Pipeline Monitoring - Stream lag, pipe status, task runs
• 📈 Automated Reports - Daily summaries, trend analysis
• 🔍 Schema Drift Detection - Track table changes over time

🐛 Troubleshooting

Connection Issues

"Connection failed" or "Authentication error"

• ✅ Verify credentials in .env file
• ✅ Check Snowflake account identifier format
• ✅ Ensure warehouse is running
• ✅ Test with: python test_connection.py

"Network timeout"

• ✅ Check firewall/VPN settings
• ✅ Verify Snowflake account is accessible
• ✅ Test connection from command line

MCP Server Issues

"Tool not found" or "Server not responding"

• ✅ Restart Claude Code after config changes
• ✅ Check MCP config file syntax (valid JSON)
• ✅ Verify file paths in MCP config are correct
• ✅ Check Claude Code logs for errors

"Permission denied"

• ✅ Verify Snowflake user has SELECT permissions
• ✅ Check database and schema names are correct
• ✅ Ensure warehouse has USAGE granted

Python/Dependency Issues

"ModuleNotFoundError: No module named 'mcp'"

• ✅ Run: pip install -r requirements.txt
• ✅ Use correct Python environment

"Snowflake connector errors"

• ✅ Update connector: pip install --upgrade snowflake-connector-python
• ✅ Check Python version (3.8+ required)

❓ Frequently Asked Questions

Do I need a Claude API key?

No! The MCP server itself does NOT require a Claude API key. Here's what you actually need:

What You Need:

1. Claude Code CLI - Authenticate once with claude auth login
2. Snowflake credentials - In your .env file
3. That's it!

How It Works:

┌─────────────────────┐
│   Claude Code CLI   │ ← Uses your Claude subscription
└──────────┬──────────┘
           │ Local communication (stdio)
┌──────────▼──────────┐
│  MCP Server         │ ← NO API key needed!
│  (server.py)        │    Just queries Snowflake
└──────────┬──────────┘
           │ Snowflake credentials only
┌──────────▼──────────┐
│   Snowflake DB      │
└─────────────────────┘

The MCP server is just a local tool provider that extends Claude Code's capabilities. It runs on your machine and only talks to Snowflake.

Cost Breakdown:

• ✅ Claude Code usage: Included in your Claude subscription
• ✅ MCP Server: Free (runs locally, no external API calls)
• ✅ Snowflake queries: Your normal Snowflake compute costs apply

Can I use this without Claude Code?

The MCP server is specifically designed to work with MCP-compatible clients like Claude Code. However, you could:

• Use it with other MCP clients (if they support MCP protocol)
• Adapt the code to work as a standalone CLI tool
• Use the Snowflake query functions directly in your own Python scripts

Is my data secure?

Yes! Here's why:

• ✅ Runs locally - MCP server runs on your machine
• ✅ Direct connection - Queries go straight to Snowflake
• ✅ No third-party servers - Your data never leaves your network
• ✅ Credentials stay local - .env file is never uploaded

Your Snowflake credentials and query results stay between your machine and Snowflake. Claude Code sees query results only when you ask it to analyze them.

What's the difference between this and Snowflake's UI?

Feature	Snowflake UI	Snowflake MCP
Query execution	✅ Manual	✅ AI-assisted
Natural language	❌ No	✅ Yes
Multi-step analysis	Manual	✅ Automated
Context across queries	❌ No	✅ Yes
Schema discovery	Manual search	✅ Ask Claude
Daily monitoring	Repetitive	✅ Can automate
Cost	Free	Free + Snowflake compute

Does this work with other data warehouses?

The current implementation is Snowflake-specific, but the pattern can be adapted for:

• PostgreSQL
• MySQL
• BigQuery
• Databricks
• Redshift
• Any database with a Python connector!

Check the Contributing section if you want to add support for other databases.

🤝 Contributing

We welcome contributions! Here's how you can help:

Ways to Contribute

• 🐛 Report bugs - Open an issue with reproduction steps
• 💡 Suggest features - Share ideas for new tools
• 📝 Improve docs - Fix typos, add examples
• 🔧 Submit PRs - Add new tools or fix issues

Development Setup

# Fork and clone
git clone https://github.com/yourusername/snowflake_mcp.git
cd snowflake_mcp

# Install dev dependencies
pip install -r requirements.txt

# Create feature branch
git checkout -b feature/my-new-tool

# Make changes and test
python test_connection.py

# Submit PR!

📜 License

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

🙏 Acknowledgments

• Built with Model Context Protocol
• Uses Snowflake Python Connector
• Inspired by the power of AI-assisted data analysis

📚 Learn More

• MCP Documentation
• Snowflake Documentation
• Claude Code Documentation
• MCP Python SDK

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Built as a learning project for integrating AI with data warehouses 🚀

Questions? Open an issue or start a discussion!