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Explore Customer Data (Snowflake)

Inspect the schema of YOUR ORGANIZATION'S OWN Snowflake data (the customer's connected warehouse), not HG Insights' datasets. Drill into one dataset: list the schemas you can access, list the tables in a schema, describe a table's columns (names, types, nullability, comments), or return a small sample of rows so you can see real values before writing SQL.

Use this when you already know which dataset you want and need its structure: to see what columns a table has, confirm column names/types before querying, or peek at a few sample rows. This is the middle step of the customer-data flow: discover (find datasets) → explore (inspect a dataset) → query (run SQL).

Do NOT use this when you need to list/find which datasets exist or get proposed field mappings — use customer_data_discover. Do NOT use this to run arbitrary SQL, aggregate, filter, or join — use customer_data_query. Do NOT use this for HG Insights firmographic/technographic/spend/intent data — those live in the company_* and hg_* tools, not the customer's own warehouse.

Scope: read-only. Access is confined to the schema configured on the Snowflake connection; a mismatched schema parameter is rejected.

Credits

Free — No credits consumed. See the full credit table for how AI Credits work.

Parameters

NameTypeDefaultDescription
actionstringlist_schemasWhat to inspect. list_schemas: the schema(s) you can access. list_tables: the tables in a schema. describe_table: a table's columns (name, type, nullability, comment) — requires table. sample_data: a few real rows from a table — requires table. Defaults to list_schemas.
schemastring``Schema to inspect. Optional: defaults to the schema configured on the Snowflake connection. If provided it must equal the configured schema (any other value is rejected) — access is confined to that one schema.
tablestring``Table (or view) name within the schema. Required for action=describe_table and action=sample_data; ignored for list_schemas and list_tables. Must be a valid Snowflake identifier.
sample_sizeinteger5How many sample rows to return. Only used by action=sample_data. Integer 1–100, default 5. Keep small — this is meant for previewing values, not bulk export.

Required Integrations

This tool is only available when your organization has the following integration configured in the Phoenix Integrations settings:

  • Snowflake (snowflake)

Use Cases

  • See what columns an account/opportunity table has before writing a query against your own Snowflake data
  • Confirm exact column names, data types, and nullability so a customer_data_query SELECT will compile
  • Preview a handful of real rows to understand how values are formatted (e.g. how "region" or "status" is encoded)
  • List the tables available in your connected schema to decide which one to query next
  • Verify which schema the Snowflake connection is scoped to before running downstream tools

Example Usage

List tables in the connected schema

{
"tool": "customer_data_explore",
"arguments": {
"action": "list_tables"
}
}

Describe a table's columns

{
"tool": "customer_data_explore",
"arguments": {
"action": "describe_table",
"table": "ACCOUNTS"
}
}

Preview 10 sample rows

{
"tool": "customer_data_explore",
"arguments": {
"action": "sample_data",
"table": "ACCOUNTS",
"sample_size": 10
}
}

Response Format

FieldTypeDescription
actionstringAction that was executed.
countnumberNumber of records returned for the action.
schemasarraySchema names returned by list_schemas.
tablesarrayTable metadata returned by list_tables.
tables[].tableNamestringTable name.
tables[].rowCountEstimatenumber | nullEstimated row count when available.
tables[].commentstring | nullTable comment.
columnsarrayColumn metadata returned by describe_table.
columns[].columnNamestringColumn name.
columns[].dataTypestringSnowflake data type.
columns[].isNullablebooleanWhether column is nullable.
columns[].commentstring | nullColumn comment.
rowsarraySample rows returned by sample_data.
executionTimeMsnumberExecution time for the action in milliseconds.

customer_data_discover, customer_data_query