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

Pitfall	Detail
Case sensitivity	Database, schema, warehouse, and role names are case-sensitive in `SNOWFLAKE_EXECUTE_SQL`.
Warehouse required for compute	SELECT and DML queries require a running warehouse. SHOW commands do not.
Multi-statement execution	Multiple statements separated by semicolons execute in sequence automatically.
SQL injection prevention	Always use the `bindings` parameter for user-supplied values to prevent injection attacks.
Pagination with LIMIT	`SHOW` commands support `limit` (max 10000) and `from_name` for cursor-based pagination.
Time Travel	Set `history: true` to include dropped objects still within the retention period.

Quick Reference

Tool Slug	Description
`SNOWFLAKE_SHOW_DATABASES`	List databases with filtering and Time Travel support
`SNOWFLAKE_SHOW_SCHEMAS`	List schemas within a database or account-wide
`SNOWFLAKE_SHOW_TABLES`	List tables with metadata (row count, size, clustering)
`SNOWFLAKE_EXECUTE_SQL`	Execute SQL: SELECT, DDL, DML with parameterized bindings

Snowflake Automation

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