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List PII Reveal Audits

lexq_pii_reveals_list
Read-only

List the PII reveal audit ledger — who revealed which fact of which trace, and when. Metadata only; revealed values are never stored or returned. Use for monthly access-log inspection and SIEM collection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number
sizeNoPage size
endDateNoEnd date (yyyy-MM-dd)
factKeyNoFilter by fact key (partial match, case-insensitive)
traceIdNoFilter by trace ID (exact match)
startDateNoStart date (yyyy-MM-dd)
revealedByNoFilter by operator ID (exact match)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / page / maximum
      Added value: +9007199254740991
  2. Added

TDQS

A4.3/5.0
Behavior4/5

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

The readOnlyHint annotation already signals a safe read operation, and the description adds meaningful behavioral context beyond that: it explicitly states that revealed values are never stored or returned and that only metadata is exposed. This gives the agent important privacy/security semantics that annotations alone do not convey.

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

Conciseness5/5

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

The description is two sentences: the first front-loads the tool's purpose and output contents, and the second adds the privacy caveat and use cases. Every sentence earns its place with no repetition or filler.

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

Completeness5/5

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

Despite having no output schema, the description tells the agent what information appears in the audit record, confirms no sensitive values are returned, and gives concrete use cases. Combined with fully documented optional parameters and the read-only annotation, the tool definition is complete enough for correct invocation and expectations.

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 100%, with all parameters already documented in the schema. The description's phrase 'which fact of which trace, and when' loosely maps to factKey, traceId, and date filters, but it does not add material parameter-specific detail beyond what the schema provides. Baseline 3 is appropriate.

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 states a specific verb ('List'), a concrete resource ('the PII reveal audit ledger'), and the exact content ('who revealed which fact of which trace, and when'). It also clarifies the non-sensitive nature of the output by saying 'Metadata only; revealed values are never stored or returned.' This clearly distinguishes it from broader list/log tools among the siblings.

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?

Explicit use cases are given: 'Use for monthly access-log inspection and SIEM collection.' This provides clear context for when the tool is appropriate. It does not name alternative sibling tools or state when not to use it, but the use-case framing is strong enough for an agent to select it correctly.

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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