generate_sql
Converts a natural language question into a SQL query based on your table schemas. The generated query is scoped to the tables accessible by the token. You can then execute it using the execute_sql tool.
get_relevant_tables_ddl
Discovers which tables are relevant to a given question using semantic search. Returns the full schema (CREATE TABLE DDL) for each matched table — useful for understanding available columns and types before generating SQL.
execute_sql
Executes a SQL query against your data warehouse and returns the results as structured data. Supports pagination for large result sets (up to 1,000 rows per page). The SQL dialect adapts automatically to your cloud — BigQuery for GCP and Nekt Express, Athena for AWS.
get_table_preview
Returns a small sample (10 rows) from a table to help understand its contents. Useful for inspecting column values, data formats, and categorical fields before writing queries.
ask_about_nekt
Answers questions about the Nekt platform itself — features, configuration, usage, and capabilities. This tool consults Nekt’s documentation and does not query your data.
list_layers
Lists all data layers (lakehouses) that your token has access to. Returns each layer’s ID, name, slug, and description — use this as the starting point when exploring files stored in Nekt volumes.
list_volumes
Lists volumes (file stores) within a layer, or across all layers if no layer is specified. Returns each volume’s name, slug, and description. Use this after list_layers to discover available file containers.