Tool Catalog

AgentQ does its work by calling tools: named, structured operations that map to specific Qualytics capabilities. A tool is closer to an API call than to a free-form instruction. AgentQ cannot browse the internet or reach any service you have not configured, and it can only call the tools in this catalog. Some of those tools accept SQL that AgentQ writes, but AgentQ never sees the results of a query.

The same catalog is available everywhere AgentQ is used:

The built-in AgentQ chat inside Qualytics.
External AI clients connected over MCP, such as Claude Desktop, ChatGPT, or Cursor.
The agentic endpoints used to build your own applications.

Two narrower assistants exist as well. The quality check and check template assistants that back the authoring forms use a small read-only subset of these tools and can only propose a definition for you to accept, never save one.

Whichever surface is used, every tool runs as the signed-in user and is subject to that user's team permissions. AgentQ has no identity or access of its own.

What Each Tool Shares with the Model

Because AgentQ sends tool results to a language model, it is worth knowing what those results contain. Every tool in this catalog falls into one of three categories.

Category	What reaches the model
None	No information about your data at all. Rule type catalogs, User Guide content, validation errors, and static guidance.
Metadata	Information about your data, but no values from it. Datastore, table, and column names, data types, descriptions, tags, row and anomaly counts, quality scores, timestamps, and quality check definitions.
Values	Metadata plus one or more individual values from your data, appearing either in a quality check failure explanation or as a profile statistic.

Three things hold across the whole catalog, regardless of category:

No tool returns your records in bulk. There is no tool that hands the model rows, result sets, or sample records. Values in the Values category appear singly and incidentally, in the same way a steward sees a value when reading why a check failed.
Masked fields stay masked. AgentQ cannot reveal the underlying value of a field you have masked. See Masked Fields below.
Credentials are never shared. Connection details and secrets for your datastores and integrations are never sent to the model.

These categories describe what tools share from your connected data sources. Some tool results also identify platform users: user_profile returns your own profile, anomaly details include assignees' names and email addresses, comments carry their author's name, an anomaly's change history names the person behind each change, and a check's details name its owner and default anomaly assignee. In the built-in AgentQ chat and through the agentic endpoints, your own name, email address, and role are also shared with the model at the start of the conversation, before any tool is called. The narrower authoring assistants do not do this, and over MCP nothing is shared until a tool runs.

The category shown for each tool below describes the most it can share. Most calls share less.

Discovery

Tool	Data	Description
`list_datastores`	Metadata	List datastores, with optional name and tag filters.
`datastore_describe`	Metadata	Get the settings and status of one datastore. Connection details are excluded.
`list_containers`	Metadata	List the tables, views, and files in a datastore.
`container_describe`	Metadata	Get the details of one container, including its profile summary.
`list_fields`	Metadata	List the fields in a container with their data types and nullability.
`field_describe`	Metadata	Get the details of one field.
`field_profile_describe`	Values	Get a field's profile statistics. See Profile statistics.
`global_search`	Metadata	Search datastores, containers, and fields by name and return ranked matches.
`user_profile`	Metadata	Get your own profile: your name, email address, role, favorites, and recent activity.

Quality Checks

Tool	Data	Description
`list_quality_check_specs`	None	List the available rule types and the properties each one accepts.
`list_quality_checks`	Metadata	List the quality checks defined on a container.
`quality_check_describe`	Metadata	Get the full definition of one quality check.
`create_quality_check`	Metadata	Create a quality check.
`update_quality_check`	Metadata	Update an existing quality check, including moving it between Draft and Active.
`validate_quality_check`	Metadata	Test a proposed change to a check without saving it.
`dry_run_quality_check`	Metadata	Run a check without recording anomalies and return counts only. It does not return the records that would fail.
`favorite_quality_check`	Metadata	Add or remove a check from your favorites.
`delete_quality_check`	Metadata	Archive a quality check, which can be restored later. By default this also removes the anomalies the check raised, unless you ask to keep them.
`list_quality_check_templates`	Metadata	List the global quality check templates.
`create_quality_check_template`	Metadata	Create a global template.
`update_quality_check_template`	Metadata	Update a global template.
`delete_quality_check_template`	Metadata	Archive a global template.

The authoring assistants add two propose-only tools, propose_quality_check and propose_quality_check_template, which return a suggested definition for your review and cannot save anything.

Computed Assets

Tool	Data	Description
`preview_query`	None	Check that a `SELECT` statement is valid before anything is created. See Validating SQL.
`create_computed_table`	Metadata	Create a computed table in a JDBC datastore.
`create_computed_file`	Metadata	Create a computed file in a file-based datastore such as S3, ADLS, or GCS.
`create_computed_join`	Metadata	Create a join across containers, including across datastores.
`create_computed_field`	Metadata	Add a derived or type-cast field to an existing container.
`describe_computed_container`	Metadata	Get the transformation definition of a computed asset, including its SQL.
`update_computed_container`	Metadata	Update a computed asset definition.
`delete_computed_container`	Metadata	Delete a computed asset.
`update_computed_field`	Metadata	Update a computed field definition.
`delete_computed_field`	Metadata	Delete a computed field.
`validate_container`	Metadata	Validate a computed asset definition without creating it.

Anomalies and Remediation

Tool	Data	Description
`list_anomalies`	Values	List anomalies, filterable by datastore, container, and status. Each anomaly's description is built from its failed check messages, which can quote a value.
`anomaly_describe`	Values	Get the full details of one anomaly, including which checks failed and why.
`get_anomaly_failed_checks`	Values	List the checks that failed for one anomaly, with their explanations.
`get_anomaly_history`	Values	Get the change history of one anomaly, including earlier versions of its description.
`update_anomaly`	Metadata	Change an anomaly's status, for example to Acknowledged.
`bulk_update_anomalies`	Metadata	Change the status of several anomalies at once.
`archive_anomaly`	Metadata	Archive an anomaly with a final resolution status.
`create_comment`	Metadata	Add a comment to an anomaly.
`list_comments`	Metadata	Read the comments on an anomaly.
`update_comment`	Metadata	Edit a comment.
`delete_comment`	Metadata	Delete a comment.

Operations and Schedules

Tool	Data	Description
`run_sync`	Metadata	Sync a datastore, discovering and registering its tables and views.
`run_catalog`	Metadata	The earlier name for `run_sync`, kept for compatibility. Prefer `run_sync`.
`run_profile`	Metadata	Profile a container.
`run_scan`	Metadata	Scan a container against its active quality checks.
`run_export`	Metadata	Export anomalies, checks, or profiles from a datastore.
`run_materialize`	Metadata	Materialize a computed asset.
`run_external_scan`	Values	Check records you supply against a container's active checks. Unlike every other tool here, the records are provided to Qualytics by the caller rather than read from your datastore. Violations create anomalies, and the built-in chat receives their descriptions in the response.
`get_operation_status`	Metadata	Poll an operation for progress and completion.
`list_operations`	Metadata	List recent operations.
`describe_operation`	Metadata	Get the details of one operation.
`abort_operation`	Metadata	Stop a running operation.
`rerun_operation`	Metadata	Run an operation again with the same settings.
`restart_operation`	Metadata	Restart an operation.
`delete_operation`	Metadata	Delete an operation record.
`list_schedules`	Metadata	List recurring schedules.
`describe_schedule`	Metadata	Get the details of one schedule.
`create_schedule`	Metadata	Create a recurring schedule for a sync, profile, scan, export, or materialize operation.
`update_schedule`	Metadata	Change a schedule.
`run_schedule`	Metadata	Trigger a scheduled operation immediately.
`set_schedule_deactivation`	Metadata	Pause or resume a schedule.
`delete_schedule`	Metadata	Delete a schedule.

Operations run in the background. These tools return an operation identifier straight away rather than waiting, so AgentQ polls for completion before moving on to a dependent step.

Stewardship

Tool	Data	Description
`update_field`	Metadata	Update a field's description, tags, or status, including marking it masked.
`bulk_update_fields`	Metadata	Update several fields at once.
`delete_field`	Metadata	Exclude or delete a field. Fields are archived rather than removed by default.
`restore_field`	Metadata	Return an excluded or masked field to active status.
`merge_fields`	Metadata	Merge two fields in the same container.
`update_container`	Metadata	Update a container's description, tags, or settings.
`bulk_update_containers`	Metadata	Update several containers at once.
`favorite_container`	Metadata	Add or remove a container from your favorites.
`delete_container`	Metadata	Exclude or delete a container. A source table or file is archived, while a computed asset is permanently deleted.
`update_container_score_settings`	Metadata	Change quality score weights and decay for a container.
`update_datastore`	Metadata	Update a datastore's description, tags, or settings.
`favorite_datastore`	Metadata	Add or remove a datastore from your favorites.
`assign_datastore_group`	Metadata	Assign a datastore to a shared group, or remove it from one.
`list_datastore_groups`	Metadata	List the available datastore groups.
`update_datastore_score_settings`	Metadata	Change quality score weights and decay for a datastore.

Insights and Scores

Tool	Data	Description
`quality_scores`	Metadata	Get quality scores across the eight dimensions for a datastore, container, or field.
`get_insights`	Metadata	Get daily metrics and trends for an asset, such as anomaly counts and score changes.
`operation_insights`	Metadata	Get operation execution counts over a date range.

Promotion

Tool	Data	Description
`promote_computed_table`	Metadata	Copy computed table definitions to another datastore.
`promote_computed_file`	Metadata	Copy computed file definitions to another datastore.
`promote_computed_field`	Metadata	Copy computed field definitions to another container.
`promote_quality_check`	Metadata	Copy quality checks to another container, landing as Draft by default.

Promotion copies definitions, not data.

Guided Workflows

Tool	Data	Description
`workflow_analyze_trends`	None	Guidance for analyzing quality score and anomaly trends over time.
`workflow_investigate_anomaly`	None	Guidance for investigating an anomaly and proposing remediation.
`workflow_interpret_quality_scores`	None	Guidance for reading quality scores in business terms.
`workflow_generate_quality_check`	None	Guidance for turning a business rule written in plain language into a check.
`workflow_transform_dataset`	None	Guidance for building a computed asset from a description.

These tools return a structured plan for approaching a task. They do not read your data or run other tools themselves. Any data involved arrives through the discovery, check, and anomaly tools that AgentQ calls as it follows the plan, and those calls are categorized above in the usual way.

Notifications and Tickets

Tool	Data	Description
`list_integrations`	Metadata	List your configured notification and ticketing integrations. Credentials are excluded.
`send_notification`	Metadata	Send a message through a configured channel such as Slack, Microsoft Teams, email, webhook, or PagerDuty.
`create_ticket`	Metadata	Create a ticket in Jira or ServiceNow, optionally linked to an anomaly.

Guidance

Tool	Data	Description
`search_userguide`	None	Search the Qualytics User Guide content bundled with your deployment. This is not a web search, and AgentQ has no other way to retrieve outside content.
`report_tool_constraint`	None	Report that a documented task cannot be completed reliably with the tools available, instead of guessing.

Masked Fields

Field masking is yours to control, and AgentQ respects it. Set a field's status to masked and the following applies:

Behavior	What happens
Distinct values in profile histograms	Replaced with a masked placeholder.
Values quoted in a quality check failure explanation	Replaced with `<masked>`.
Expected value checks	Never suggested for a masked field.
Unmasking	Not available to AgentQ in any form.

Exporting or materializing with masked values included is a separate, permission-gated action on the operation itself. A dry run (dry_run_quality_check) can also be asked to include masked values, and the request goes through the same permission checks; either way, the dry run only returns summary counts to the model, never the values themselves. AgentQ will not request masked values unless you ask in the conversation, and it will not carry that request forward into later turns.

Profile statistics

field_profile_describe returns the statistics Qualytics computes when profiling a field: minimum, maximum, mean, sum, quartiles, standard deviation, and length extremes. These scalar statistics are numeric, so for text fields only the length and count statistics are populated.

Histogram buckets are the one part of a profile that contains literal values from the field. They are omitted unless explicitly requested, and for a masked field they are replaced with a placeholder.

Validating SQL

preview_query confirms that a statement is valid before a computed asset is created. To do that it runs the statement against your datastore with a row limit, under your own permissions. The results are used only to determine whether the query works, and are then discarded. No rows and no column names are returned to the model, and nothing is created or saved.

Only SELECT statements and WITH clauses are accepted. See AgentQ Limits for the full list of blocked statements.

Permissions

Every tool checks your team permissions before it acts, using the same rules as the rest of the platform:

Kind of tool	Typical requirement
Reading assets, profiles, scores, checks, anomalies, operations, and schedules	Reporter team permission
Reading and writing comments, and checking an operation's progress	Viewer team permission
Creating a check as a Draft, and dry running a check	Drafter team permission
Creating a check as Active, and updating, validating, or deleting a check	Author team permission
Updating an anomaly's status, tags, assignees, or description	Author team permission
Archiving an anomaly	Editor team permission
Creating, changing, and deleting computed assets, fields, containers, datastores, and score settings	Editor team permission
Running operations, and creating, changing, running, pausing, or deleting schedules	Editor team permission
Deleting an operation record	Editor team permission, and you must have started the operation yourself unless you have the Admin role
Managing global templates and global tags, and sending through integrations	Manager or Admin role

Team permissions build on one another: Editor includes Author, Author includes Drafter, Drafter includes Viewer, and Viewer includes Reporter, so a higher level always meets a lower requirement. Comments are personal: you can edit or delete only your own, although an Admin can delete any comment.

If you lack the permission for a tool, AgentQ tells you the action was not permitted rather than working around it. It cannot act outside your access, and it cannot grant itself more. The one exception is the Admin role, which has access to every datastore and is not subject to team permission checks.

Writes take effect as soon as a tool runs. There is no confirmation step built into the tools, so AgentQ is instructed to get your agreement before anything destructive or irreversible. What a deletion does depends on the tool. Deleting a field, a quality check, or a check template archives it rather than removing it, and an archived field or check can be restored. Deleting a check also removes the anomalies it raised by default, unless you ask to keep them. The same goes for deleting a source table or file, while deleting a computed asset, a computed field, a tag, a schedule, a comment, or an operation record is permanent and cannot be undone. The validate and dry run tools let you check a definition before committing to it.

AgentQ in Action for how these tools are surfaced during a conversation.
MCP for how external clients discover and call them.
AgentQ Limits for rate limits, listing limits, timeouts, and SQL constraints.
Agentic Endpoints for building your own applications on these capabilities.

Tool	Data	Description
`manage_tags`	Metadata	Apply, remove, or replace tags on datastores, containers, fields, and checks.
`list_tags`	Metadata	List global tags.
`describe_tag`	Metadata	Get the details of one global tag.
`create_tag`	Metadata	Create a global tag.
`update_tag`	Metadata	Update a global tag.
`delete_tag`	Metadata	Delete a global tag.