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mcp-snowflake-server

MCP.Pizza Chef: isaacwasserman

The mcp-snowflake-server is a Model Context Protocol (MCP) server designed to facilitate direct interaction with Snowflake databases. It allows running SQL queries through exposed tools and provides continuous, real-time access to data insights and schema context. Key features include memo resources that aggregate data insights dynamically and per-table schema summaries that detail columns and comments when prefetch is enabled. This server is ideal for integrating Snowflake data into AI workflows, enabling models to query, analyze, and understand database structures and contents efficiently.

Use This MCP server To

Run SQL queries on Snowflake databases via MCP tools Expose Snowflake table schema summaries as model-readable resources Aggregate and update data insights continuously for AI context Integrate Snowflake data querying into AI-enhanced workflows Provide real-time database context to language models Enable AI agents to interact with Snowflake for data retrieval

README

Snowflake MCP Server

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Overview

A Model Context Protocol (MCP) server implementation that provides database interaction with Snowflake. This server enables running SQL queries via tools and exposes data insights and schema context as resources.


Components

Resources

  • memo://insights
    A continuously updated memo aggregating discovered data insights.
    Updated automatically when new insights are appended via the append_insight tool.

  • context://table/{table_name}
    (If prefetch enabled) Per-table schema summaries, including columns and comments, exposed as individual resources.


Tools

The server exposes the following tools:

Query Tools

  • read_query
    Execute SELECT queries to read data from the database.
    Input:

    • query (string): The SELECT SQL query to execute
      Returns: Query results as array of objects
  • write_query (enabled only with --allow-write)
    Execute INSERT, UPDATE, or DELETE queries.
    Input:

    • query (string): The SQL modification query
      Returns: Number of affected rows or confirmation
  • create_table (enabled only with --allow-write)
    Create new tables in the database.
    Input:

    • query (string): CREATE TABLE SQL statement
      Returns: Confirmation of table creation

Schema Tools

  • list_databases
    List all databases in the Snowflake instance.
    Returns: Array of database names

  • list_schemas
    List all schemas within a specific database.
    Input:

    • database (string): Name of the database
      Returns: Array of schema names
  • list_tables
    List all tables within a specific database and schema.
    Input:

    • database (string): Name of the database
    • schema (string): Name of the schema
      Returns: Array of table metadata
  • describe_table
    View column information for a specific table.
    Input:

    • table_name (string): Fully qualified table name (database.schema.table)
      Returns: Array of column definitions with names, types, nullability, defaults, and comments

Analysis Tools

  • append_insight
    Add new data insights to the memo resource.
    Input:
    • insight (string): Data insight discovered from analysis
      Returns: Confirmation of insight addition
      Effect: Triggers update of memo://insights resource

Usage with Claude Desktop

Installing via Smithery

To install Snowflake Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install mcp_snowflake_server --client claude

Installing via UVX

"mcpServers": {
  "snowflake_pip": {
    "command": "uvx",
    "args": [
      "--python=3.12",  // Optional: specify Python version <=3.12
      "mcp_snowflake_server",
      "--account", "your_account",
      "--warehouse", "your_warehouse",
      "--user", "your_user",
      "--password", "your_password",
      "--role", "your_role",
      "--database", "your_database",
      "--schema", "your_schema"
      // Optionally: "--allow_write"
      // Optionally: "--log_dir", "/absolute/path/to/logs"
      // Optionally: "--log_level", "DEBUG"/"INFO"/"WARNING"/"ERROR"/"CRITICAL"
      // Optionally: "--exclude_tools", "{tool_name}", ["{other_tool_name}"]
    ]
  }
}

Installing Locally

  1. Install Claude AI Desktop App

  2. Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh
  1. Create a .env file with your Snowflake credentials:
SNOWFLAKE_USER="xxx@your_email.com"
SNOWFLAKE_ACCOUNT="xxx"
SNOWFLAKE_ROLE="xxx"
SNOWFLAKE_DATABASE="xxx"
SNOWFLAKE_SCHEMA="xxx"
SNOWFLAKE_WAREHOUSE="xxx"
SNOWFLAKE_PASSWORD="xxx"
# Alternatively, use external browser authentication:
# SNOWFLAKE_AUTHENTICATOR="externalbrowser"
  1. [Optional] Modify runtime_config.json to set exclusion patterns for databases, schemas, or tables.

  2. Test locally:

uv --directory /absolute/path/to/mcp_snowflake_server run mcp_snowflake_server
  1. Add the server to your claude_desktop_config.json:
"mcpServers": {
  "snowflake_local": {
    "command": "/absolute/path/to/uv",
    "args": [
      "--python=3.12",  // Optional
      "--directory", "/absolute/path/to/mcp_snowflake_server",
      "run", "mcp_snowflake_server"
      // Optionally: "--allow_write"
      // Optionally: "--log_dir", "/absolute/path/to/logs"
      // Optionally: "--log_level", "DEBUG"/"INFO"/"WARNING"/"ERROR"/"CRITICAL"
      // Optionally: "--exclude_tools", "{tool_name}", ["{other_tool_name}"]
    ]
  }
}

Notes

  • By default, write operations are disabled. Enable them explicitly with --allow-write.
  • The server supports filtering out specific databases, schemas, or tables via exclusion patterns.
  • The server exposes additional per-table context resources if prefetching is enabled.
  • The append_insight tool updates the memo://insights resource dynamically.

License

MIT

mcp-snowflake-server FAQ

How does mcp-snowflake-server expose Snowflake data to models?
It provides resources like memo://insights for aggregated data insights and context://table/{table_name} for schema summaries, enabling models to access structured database context.
Can I run custom SQL queries using mcp-snowflake-server?
Yes, the server exposes query tools that allow running SQL queries directly on Snowflake databases.
What is the purpose of the memo://insights resource?
It continuously aggregates and updates discovered data insights, providing a dynamic context resource for models.
How does schema prefetching work in mcp-snowflake-server?
When enabled, it prefetches and exposes per-table schema summaries including columns and comments as individual resources.
Is mcp-snowflake-server compatible with multiple LLM providers?
Yes, it is designed to work with various LLMs like OpenAI, Anthropic Claude, and Google Gemini by exposing structured Snowflake data context.
How do I update the insights in the memo resource?
Insights are appended automatically via the append_insight tool exposed by the server.
What kind of tools does the server expose?
It exposes SQL query tools and insight management tools to interact with Snowflake data.
Can this server be integrated into existing MCP client workflows?
Yes, it is designed as a lightweight MCP server to be easily integrated into MCP client orchestrations.