mcp-snowflake-server

  • Databases
Python
2
-
security - not tested
F
license - not found
-
quality - not tested

Snowflake integration implementing read and (optional) write operations as well as insight tracking

  1. Tools
  2. Prompts
  3. Resources
  4. Server Configuration
  5. README.md

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Tools

Functions exposed to the LLM to take actions

NameDescription

No tools

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
SNOWFLAKE_ROLEYesYour Snowflake role
SNOWFLAKE_USERYesYour Snowflake username
SNOWFLAKE_SCHEMAYesYour Snowflake schema
SNOWFLAKE_ACCOUNTYesYour Snowflake account identifier
SNOWFLAKE_DATABASEYesYour Snowflake database
SNOWFLAKE_PASSWORDYesYour Snowflake password
SNOWFLAKE_WAREHOUSEYesYour Snowflake warehouse
README.md

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 with tools and intereacting with a memo of data insights presented as a resource.

Components

Resources

The server exposes a single dynamic resource:

  • memo://insights: A continuously updated data insights memo that aggregates discovered insights during analysis
    • Auto-updates as new insights are discovered via the append-insight tool

Tools

The server offers six core 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 (with --allow-write flag)
    • Execute INSERT, UPDATE, or DELETE queries
    • Input:
      • query (string): The SQL modification query
    • Returns: { affected_rows: number }
  • create_table (with --allow-write flag)
    • Create new tables in the database
    • Input:
      • query (string): CREATE TABLE SQL statement
    • Returns: Confirmation of table creation

Schema Tools

  • list_tables
    • Get a list of all tables in the database
    • No input required
    • Returns: Array of table names
  • describe-table
    • View column information for a specific table
    • Input:
      • table_name (string): Name of table to describe (can be fully qualified)
    • Returns: Array of column definitions with names and types

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
    • 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

This server can be run without cloning or installing the repository.

# Add the server to your claude_desktop_config.json "mcpServers": { "snowflake": { "command": "uvx", "args": [ "mcp_snowflake_server" # Optionally: "--allow-write" (but not recommended) ], "env": { "SNOWFLAKE_WAREHOUSE": "your_warehouse", "SNOWFLAKE_DATABASE": "your_database", "SNOWFLAKE_ACCOUNT": "your_account_identifier", "SNOWFLAKE_USER": "your_username", "SNOWFLAKE_ROLE": "your_role", "SNOWFLAKE_SCHEMA": "your_schema", "SNOWFLAKE_PASSWORD": "your_password" } } }

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues with dependencies of the server.
  • Extract server characteristics such as tools, resources, prompts, and required parameters.

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