Native tools

AIDB ships with a large built-in catalog of native tools — mostly thin wrappers around existing AIDB functionality, so an agent can operate on pipelines, knowledge bases, models, and volumes the same way you would from SQL directly. This is one of three tool types AIDB supports — see Tools reference for the full catalog, including Custom SQL tools and MCP tools . sleep is always available to every agent regardless of its configured tools list — unless the backing model caps how many tools a request can carry and the agent’s other configured tools already fill that cap — since it backs the reasoning loop’s own ability to pause and resume. Every other native tool must be explicitly listed in tools to be usable.

A tool’s exact parameters are always visible with SELECT params FROM aidb.tools WHERE name = '<name>' ; the tables below give the one-line description and read-only status for each.

Ad hoc SQL

Tool	Read-only	Description
run_sql_query	Yes	Run a read-only SQL query against the database and return its rows as JSON.

Catalog discovery

Introspection of the database catalog: these tools let an agent discover what schemas, tables, views, and functions exist, without having to construct information_schema /pg_catalog queries itself.

They’re related to the Semantic knowledge bases below, but the two are complementary rather than interchangeable:

• Catalog discovery tools do live, exact lookups against the Postgres catalog. They need no setup, always reflect the current state of the database, and match by exact name or SQL LIKE pattern.

• Semantic knowledge base tools run semantic (vector) search over a crawled snapshot of the schema. They require a pre-built, opt-in knowledge base (and a re-crawl to pick up structural changes), but can answer natural-language questions like “which table holds customer contact info?” that exact catalog lookups can’t.

A typical agent uses both: semantic search to find the right table from a vague description, then catalog discovery to get its exact, current columns and foreign keys.

All four tools are privilege-aware — they only return objects the calling user can actually see. Listings hide Postgres and AIDB internal schemas as well as installed extensions’ schemas and objects by default (catalog_get_object_details deliberately doesn’t hide extension objects: explicitly naming one is asking for it). Listings are capped at 200 rows; every row carries a truncated flag that’s true when more rows exist, signaling the caller to narrow the search with schema or name_pattern . Optional text parameters treat an empty string the same as an omitted argument.

Tool	Read-only	Description
catalog_list_schemas	Yes	List schemas in the current database. Hides Postgres/AIDB internal schemas and installed extensions' schemas unless include_system is set.
catalog_list_objects	Yes	List tables, views, materialized views, foreign tables, and/or functions. Narrow with schema, object_type, and/or name_pattern.
catalog_get_object_details	Yes	Get full detail on one object by name: columns, primary/unique/foreign keys for relations; arguments and return type for functions.
catalog_list_relations	Yes	List foreign-key relationships — every FK in a schema, or only edges touching one specific table, in either direction.

Semantic knowledge bases

Schema-search knowledge bases to allow semantic search based discovery of tables, columns, and other DB objects. Distinct from the knowledge bases for user data that pipelines can create.

Tool	Read-only	Description
create_semantic_kb	No	Create a semantic knowledge base over one or more schemas for natural-language schema search.
delete_semantic_kb	No	Delete a semantic knowledge base by name.
list_semantic_kbs	Yes	List all registered semantic knowledge bases.
get_semantic_kb	Yes	Get a registered semantic knowledge base's configuration by name.
refresh_semantic_kb	No	Re-crawl a semantic knowledge base's schemas to pick up structural changes.
update_semantic_kb_auto_processing	No	Change a semantic knowledge base's auto-processing mode.
get_column_definitions	Yes	Search a semantic knowledge base for matching column/table definitions.
get_metadata	Yes	Search a semantic knowledge base's full schema metadata (columns, tables, comments).
get_entity_definitions	Yes	Search a semantic knowledge base for matching table/view definitions.
semantic_kb_search	Yes	Run a composite semantic search over a knowledge base, returning one ranked list of matching tables, views, columns, and aliases with a source-type discriminator.
search_by_comment	Yes	Search a semantic knowledge base's schema/table/column comments by similarity.
semantic_kb_stats	Yes	Get row/entity counts for a semantic knowledge base.

Models

See Integrating models .

Tool	Read-only	Description
list_models	Yes	List all registered models.
get_model	Yes	Get a registered model's configuration by name.
create_model	No	Register a new model with a provider and configuration.
delete_model	No	Delete a registered model by name.
sync_hcp_models	No	Sync models available from the configured Hybrid Manager (HM) into the model registry.
list_hcp_models	Yes	List models already synced from HM.
get_hcp_models	Yes	List models available from HM, synced or not.
create_hcp_model	No	Register a model from HM under a local name.

Volumes

See Configuring external storage .

Tool	Read-only	Description
list_volumes	Yes	List all registered volumes.
create_volume	No	Register a new volume backed by an object store.
delete_volume	No	Delete a volume by name.
list_volume_content	Yes	List the files stored in a volume.
read_volume_file	Yes	Read a file's contents from a volume.
write_volume_data	No	Write data to a file in a volume.
delete_volume_file	No	Delete a file from a volume.

Embedding and document processing

See SQL functions .

Tool	Read-only	Description
encode_text	Yes	Compute a text embedding vector for document/search-index storage.
encode_text_query	Yes	Compute a text embedding vector tuned for querying (vs. indexing).
encode_image	Yes	Compute an image embedding vector.
generate_text	Yes	Generate text from a language model.
get_model_description	Yes	Get a human-readable description of a model.
list_model_providers	Yes	List the foreign-server-backed model providers available for registering models against.
rerank_text	Yes	Rerank a list of candidate texts against a query using a reranker model.
get_adapter_embedding_dimensions	Yes	Get a model's output embedding dimensionality for text or image input.
chunk_text	Yes	Split text into chunks suitable for embedding.
parse_html	Yes	Parse HTML into plaintext or markdown.
parse_pdf	Yes	Extract text from a PDF, one row per page.
pdf_to_image	Yes	Render a PDF's pages to images, one row per page.
perform_ocr	Yes	Run OCR over an image or scanned document.
summarize_text	Yes	Summarize text using a language model.

Agent management

See Managing agents . Letting an agent create or reconfigure other agents (including itself) is a powerful, self-modifying capability — grant these tools deliberately.

Tool	Read-only	Description
list_agents	Yes	List all registered agents.
create_agent	No	Create a new agent. Returns a single row with an error column (NULL on success).
update_agent	No	Update an existing agent's configuration. Only fields explicitly passed are changed.
delete_agent	No	Delete an agent by name. Returns a single row with an error column (NULL on success).
sleep	Yes	Pause reasoning for a number of seconds, then automatically resume. Always available to every agent, regardless of its configured tool list, unless the model's own tool-count cap is already filled by the agent's other configured tools.

Pipelines

See AI pipelines .

Tool	Read-only	Description
search_pipelines	Yes	List all registered data pipelines.
get_pipeline	Yes	Get a registered data pipeline's configuration by name.
delete_pipeline	No	Delete a registered data pipeline by name.
run_pipeline	No	Manually run a pipeline once, processing any pending source rows.
get_pipeline_metrics	Yes	Get a pipeline's processing status and row counts.
get_error_logs	Yes	List a pipeline's record-level processing errors, optionally filtered.
clear_error_logs	No	Delete specific pipeline error log entries by id.
requeue_pipeline_errors	No	Mark specific pipeline errors' source rows dirty so the next run retries them.
get_error_log_summary	Yes	Get error counts for a pipeline, grouped by step, operation, and category.
get_all_error_summaries	Yes	Get error counts across every pipeline, grouped by step, operation, and category.

!!!note Pipeline authoring (aidb.create_pipeline /aidb.update_pipeline ) has no native tool. Those functions take up to 30 parameters across step/step-options pairs — impractical for a single generic tool call — so multi-step pipeline authoring isn’t currently something an agent can do on its own.