Snowflake Automation

What This Skill Does

The Snowflake Automation skill enables users to automate data warehouse workflows within Snowflake. It allows for discovering databases, browsing schemas and tables, executing SQL queries, and integrating Snowflake data operations into cross-app pipelines. This skill is ideal for users who need to manage and interact with their Snowflake data programmatically.

When to Use

• List available databases in Snowflake.
• Browse schemas within a Snowflake database.
• List tables within a Snowflake schema.
• Execute SQL queries (SELECT, DDL, DML).
• Automate data extraction from Snowflake.
• Integrate Snowflake with other applications.

Key Features

• Automates Snowflake data warehouse workflows.
• Discovers databases using SNOWFLAKE_SHOW_DATABASES.
• Browses schemas using SNOWFLAKE_SHOW_SCHEMAS.
• Lists tables using SNOWFLAKE_SHOW_TABLES.
• Executes arbitrary SQL queries.
• Integrates Snowflake data operations into pipelines.

Snowflake Automation

Automate your Snowflake data warehouse workflows -- discover databases, browse schemas and tables, execute arbitrary SQL (SELECT, DDL, DML), and integrate Snowflake data operations into cross-app pipelines.

Toolkit docs: composio.dev/toolkits/snowflake

Setup

Add the Composio MCP server to your client: https://rube.app/mcp Connect your Snowflake account when prompted (account credentials or key-pair authentication) Start using the workflows below

Core Workflows

1. List Databases

Use SNOWFLAKE_SHOW_DATABASES to discover available databases with optional filtering and Time Travel support.

Tool: SNOWFLAKE_SHOW_DATABASES Inputs:

• like_pattern: string (SQL wildcard, e.g., "%test%") -- case-insensitive
• starts_with: string (e.g., "PROD") -- case-sensitive
• limit: integer (max 10000)
• history: boolean (include dropped databases within Time Travel retention)
• terse: boolean (return subset of columns: created_on, name, kind, database_name, schema_name)
• role: string (role to use for execution)
• warehouse: string (optional, not required for SHOW DATABASES)
• timeout: integer (seconds)

2. Browse Schemas

Use SNOWFLAKE_SHOW_SCHEMAS to list schemas within a database or across the account.

Tool: SNOWFLAKE_SHOW_SCHEMAS Inputs:

• database: string (database context)
• in_scope: "ACCOUNT" | "DATABASE" | "<specific_database_name>"
• like_pattern: string (SQL wildcard filter)
• starts_with: string (case-sensitive prefix)
• limit: integer (max 10000)
• history: boolean (include dropped schemas)
• terse: boolean (subset columns only)
• role, warehouse, timeout: string/integer (optional)

3. List Tables

Use SNOWFLAKE_SHOW_TABLES to discover tables with metadata including row counts, sizes, and clustering keys.

Tool: SNOWFLAKE_SHOW_TABLES Inputs:

• database: string (database context)
• schema: string (schema context)
• in_scope: "ACCOUNT" | "DATABASE" | "SCHEMA" | "<specific_name>"
• like_pattern: string (e.g., "%customer%")
• starts_with: string (e.g., "FACT", "DIM", "TEMP")
• limit: integer (max 10000)
• history: boolean (include dropped tables)
• terse: boolean (subset columns only)
• role, warehouse, timeout: string/integer (optional)

4. Execute SQL Statements

Use SNOWFLAKE_EXECUTE_SQL for SELECT queries, DDL (CREATE/ALTER/DROP), and DML (INSERT/UPDATE/DELETE) with parameterized bindings.

Tool: SNOWFLAKE_EXECUTE_SQL Inputs:

• statement: string (required) -- SQL statement(s), semicolon-separated for multi-statement
• database: string (case-sensitive, falls back to DEFAULT_NAMESPACE)
• schema_name: string (case-sensitive)
• warehouse: string (case-sensitive, required for compute-bound queries)
• role: string (case-sensitive, falls back to DEFAULT_ROLE)
• bindings: object (parameterized query values to prevent SQL injection)
• parameters: object (Snowflake session-level parameters)
• timeout: integer (seconds; 0 = max 604800s)

Examples: "SELECT * FROM my_table LIMIT 100;" "CREATE TABLE test (id INT, name STRING);" "ALTER SESSION SET QUERY_TAG='mytag'; SELECT COUNT(*) FROM my_table;"

Known Pitfalls

Quick Reference