Snowflake Automation

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: claude-skills.ai/toolkits/snowflake

Setup

1. Add the Claude Skill MCP server to your client: https://rube.app/mcp
2. Connect your Snowflake account when prompted (account credentials or key-pair authentication)
3. 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

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