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CoveoSec
by CoveoSec

xql_lookups_remove_data

Remove data from a lookup dataset by specifying filter blocks with AND logic. Deletion occurs only when all filter sets are found; otherwise no data is deleted.

Instructions

Remove data from a lookup dataset

Remove data from a dataset based on the specified parameters. If any one of the filter sets are not found, the API does not delete any data.

The /public_api/xql/lookups/remove_data/ endpoint does not support concurrent edits. Sending concurrent calls to this endpoint can cause data to be unintentionally overwritten or deleted. To allow sufficient time for each API call to complete its operation before initiating another one, assume that 1000 entries can be added per API every 10 seconds.

Note:

  • All lookup entries matching any of the filter blocks are deleted. To match a filter block, a lookup entry must match all the specified fields as if there were an AND operator between them.

  • Requests time out after three minutes.

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

[POST /public_api/v1/xql/lookups/remove_data] · Lookup Datasets

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
request_dataNo
Behavior5/5

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It discloses concurrency limitations, all-or-nothing deletion behavior, AND matching semantics, timeout, and required licenses. This is exceptionally transparent.

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 somewhat verbose but well-structured with headers, notes, and bold text. The first two sentences are slightly redundant, but the additional behavioral warnings are valuable and justify the length.

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

Completeness4/5

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

The description covers key aspects of a complex delete operation: concurrency, atomicity, matching logic, timeout, and licensing. However, it omits return value details and error handling, and the 'filter blocks' ambiguity leaves some structural gaps.

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

Parameters2/5

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

Schema description coverage is 0% for the top-level parameter, and the description does not compensate. It uses ambiguous terms like 'filter blocks' that may not align with the schema's single 'filters' object, and it does not explicitly name or explain the 'request_data' structure beyond vague references to 'specified parameters'.

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

Purpose5/5

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

The description clearly states 'Remove data from a lookup dataset' with a specific verb and resource. It distinguishes from sibling tools like xql_lookups_add_data and xql_lookups_get_data by focusing on removal.

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?

The description provides clear context for when to use the tool (removing data based on filters) and includes important behavioral constraints like concurrency and all-or-nothing semantics. It does not explicitly mention alternatives or exclusions, but the context is sufficient for decision-making.

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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