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CoveoSec
by CoveoSec

xql_delete_dataset

Delete a dataset by name from XSIAM. Optionally force deletion when dependencies exist, but protected datasets cannot be removed.

Instructions

Delete a dataset

Delete a dataset with the specified name. The following dataset types can be deleted: Lookup, Raw, User, Snapshot, and Correlation. You can only delete a dataset with dependencies by setting force to TRUE.

Note: The System dataset and other protected datasets cannot be deleted.

Required license: Cortex XSIAM Premium or Cortex XSIAM Enterprise or Cortex XSIAM Enterprise Plus

[POST /public_api/v2/xql/delete_dataset] · Dataset Management

Example request body:

{
  "dataset_name": "users",
  "force": "yes"
}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
request_dataNo
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It does disclose the license requirement, protected dataset restrictions, and force behavior, but it omits other behavioral details such as idempotency, failure modes, or whether deletion is reversible. The provided details are useful but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear first sentence, a note, license info, endpoint, and an example. It is somewhat repetitive ('Delete a dataset' appears twice) but each section adds useful context without excessive fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a destructive operation with no annotations and no output schema, the description covers key constraints, dependencies, and license, but it does not explain the response format, error behavior for nonexistent datasets, or the exact request body structure. The example helps but leaves gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It does mention 'dataset_name' via 'specified name' and 'force' for dependencies, and the example body illustrates usage. However, the example uses `"force": "yes"` while the schema declares boolean, and it does not explain the `request_data` wrapper structure clearly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Delete a dataset') and specifies the resource ('dataset with the specified name'), plus lists supported dataset types. However, it does not differentiate from the sibling tool 'dataset_delete_dataset', so it is clear but lacks sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-not guidance: protected datasets cannot be deleted, and force is required only when dependencies exist. Also mentions required licenses. However, it does not mention alternatives or contrast with similar delete tools, falling short of the top score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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