Setup
- In Agent Studio, go to Tools → Snowflake → Connect
- Sign in with your Snowflake account and authorize access
- Review the requested permissions and click Allow
Available actions
| Action | Description |
|---|---|
SNOWFLAKE__LIST_DATABASES | Lists all user-created databases in the connected Snowflake account, excluding system databases. |
SNOWFLAKE__SHOW_SCHEMAS | Lists all schemas within a specified Snowflake database. |
SNOWFLAKE__SHOW_TABLES | Lists all tables within a specified schema in a Snowflake database. |
SNOWFLAKE__DESCRIBE_TABLE | Returns the column definitions and structure of a specified Snowflake table. |
SNOWFLAKE__EXPLORE_COLUMNS | Retrieves detailed column metadata for a table by querying the INFORMATION_SCHEMA.COLUMNS view. |
SNOWFLAKE__RUN_QUERY | Executes any SQL statement, including queries, DDL, and DML, against a Snowflake database. |
Adding to an agent
- Open your agent in Agent Studio → Agent Builder
- Go to Tools and enable Snowflake
- Select only the actions your agent needs
- Set auth mode: Shared (agent acts on one account) or Per-user (each end-user connects their own account)
Example use cases
Automated data reporting An agent usesSNOWFLAKE__RUN_QUERY to pull sales or usage metrics on a schedule and formats the results into a summary report, removing the need for manual SQL runs.
Schema discovery for data onboarding
An agent calls SNOWFLAKE__LIST_DATABASES, SNOWFLAKE__SHOW_SCHEMAS, and SNOWFLAKE__SHOW_TABLES in sequence to map out the full structure of a Snowflake environment, helping new team members or downstream tools understand what data is available.
Dynamic query generation from natural language
An agent uses SNOWFLAKE__DESCRIBE_TABLE and SNOWFLAKE__EXPLORE_COLUMNS to understand a table’s structure, then constructs and runs a precise SQL query with SNOWFLAKE__RUN_QUERY in response to a plain-English question from the user.