#Data warehouses

Analytics warehouse connectors. For transactional databases see
Databases.

Both warehouse connectors default to read-only: a regex-based
validator rejects any statement whose first keyword is outside the
connector's allowed read set, and any compound statement (multiple
semicolons) or mutation keyword inside the body is rejected as well.
Snowflake allows SELECT, WITH, SHOW, DESCRIBE (DESC),
EXPLAIN, and USE; BigQuery allows the narrower SELECT, WITH,
and EXPLAIN.

Both connectors follow the same agent-ready pattern as the database
connectors: catalog list tools return compact summary envelopes with
stable ordinal refs (dataset_ref, table_ref, schema_ref,
warehouse_ref, etc.) plus the user-facing names (dataset_id,
table_id, name, database_name, schema_name). Default
max_results is 25 for list tools and 100 for query tools;
table-preview tools default to 5 rows. Pass include_metadata=False
to receive the raw provider response untouched.

#SnowflakeToolSet

SnowflakeToolSet wraps Snowflake's SQL API v2 (HTTP).
Authenticate with a Snowflake-issued JWT or OAuth bearer token.

from maivn_tools import SnowflakeToolSet connector = SnowflakeToolSet(    account="xy12345.us-east-1",    token=secrets["SNOWFLAKE_JWT"],    warehouse="ANALYTICS_WH",    database="ANALYTICS",    schema="PUBLIC",)

Tools: run_query(sql, bindings), submit_async_query(sql, bindings),
get_statement(statement_handle, partition),
cancel_statement(statement_handle) (destructive),
list_databases(max_results),
list_schemas(database, max_results),
list_tables(database, schema, max_results),
list_views(database, schema, max_results),
describe_table(name, database, schema),
list_warehouses(max_results), server_version.

run_query and submit_async_query validate the SQL is read-only
before submission. Identifier-taking tools validate names against
^[A-Za-z_][A-Za-z0-9_$]*$ so they cannot inject SQL.

#Agent-ready behavior

• list_databases, list_schemas, list_tables, list_views, and
  list_warehouses return envelopes with ordinal refs (database_ref,
  schema_ref, table_ref, view_ref, warehouse_ref) and the
  user-facing SHOW-row columns (name, database_name,
  schema_name, kind, owner, plus size / state for warehouses).
  Default max_results is 25.
• describe_table(name, database, schema) returns the raw Snowflake
  DESCRIBE TABLE payload (column rows under data with names under
  resultSetMetaData.rowType).
• run_query / submit_async_query return the raw SQL API envelope
  ({"resultSetMetaData": {...}, "data": [...], "statementHandle": ...}).
• Destructive tools: cancel_statement (aborts an in-flight statement;
  partial result partitions may be discarded).

Example:

tables = connector.list_tables(database="ANALYTICS", schema="PUBLIC")# {"tables": [{"table_ref": "table_1", "name": "ORDERS", "database_name": "ANALYTICS", "schema_name": "PUBLIC", "kind": "TABLE", "rows": 10000, "bytes": 1234567}, ...], "returned": 25, "total": 47, "truncated": True}

#BigQueryToolSet

BigQueryToolSet wraps the BigQuery REST API v2.
Authenticate with a Google OAuth access token (typically from a service
account with the BigQuery Data Viewer + BigQuery Job User roles, or
the appropriate bigquery.* scopes).

from maivn_tools import BigQueryToolSet connector = BigQueryToolSet(    project_id="my-gcp-project",    token=token_cache,)

Tools: list_datasets(max_results, include_metadata, ...),
get_dataset(dataset_id),
list_tables(dataset_id, max_results, include_metadata, ...),
get_table(dataset_id, table_id),
get_table_data(dataset_id, table_id, max_results, ...),
run_query(sql, max_results, ...),
get_query_results(job_id, max_results, ...),
get_job(job_id, location), list_jobs(max_results, ...),
cancel_job(job_id, location) (destructive),
list_routines(dataset_id, max_results, ...),
list_models(dataset_id, max_results, ...).

run_query rejects anything that is not a SELECT, WITH, or
EXPLAIN statement. get_table_data is the cheapest way to fetch raw
rows; run_query should be reserved for analytics-style queries where
billing-per-byte-scanned is acceptable.

#Agent-ready behavior

• list_datasets and list_tables return compact summaries by default
  with stable refs (dataset_ref, table_ref) plus the user-facing
  dataset_id, table_id, parent project_id, location, type
  (TABLE / VIEW / MATERIALIZED_VIEW / EXTERNAL),
  friendly_name, and labels. Pass include_metadata=False for the
  raw BigQuery list response. nextPageToken is preserved for paging.
• get_table_data defaults to 5 rows so quick previews stay
  cheap; raise max_results for larger samples or use run_query with
  a LIMIT.
• run_query / get_query_results default to 100 rows per page.
• list_jobs, list_routines, list_models default to 25 and
  return the raw BigQuery list response (these are inspection tools
  used less often than dataset / table exploration).
• Destructive tools: cancel_job (aborts an in-flight job; partial
  destination-table writes from long loads may be left behind).

Example:

datasets = connector.list_datasets()# {"datasets": [{"dataset_ref": "dataset_1", "dataset_id": "analytics", "project_id": "my-gcp-project", "location": "US", ...}, ...]}tables = connector.list_tables(dataset_id=datasets["datasets"][0]["dataset_id"])