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audit_robustness

Test slicer-style columns for Power BI visual-breaking errors and blank results, pinpointing exact failure values before shipping.

Instructions

The complete 'select a value and the visuals fall over' detector - the pre-delivery reliability gate. For every low-cardinality slicer-style column it catches BOTH failure modes: (1) ERROR-on-select - a measure throws in that filter context, breaking the canvas (e.g. 'Brand=a given brand breaks [Margin %]'); and (2) BLANK-on-select - a value that empties every visual because it has no underlying data (the empty-brand class - a member with no rows). Pinpoints the offending values. Run before shipping. Tune with maxColumns/maxValuesPerColumn/maxMeasures; set anchorMeasure to choose the measure used for the blank test (defaults to the first model measure).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sessionIdYes
maxColumnsNomax slicer-style columns to test (default 20)
maxMeasuresNomax measures to test (default 40)
anchorMeasureNomeasure name used for the blank-on-select test (default: first model measure, e.g. a primary sales measure)
maxValuesPerColumnNoonly test columns with at most this many distinct values (default 200)
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It thoroughly explains the two detection modes and that it pinpoints offending values, implying a read-only analysis. It doesn't explicitly state that it doesn't modify the model, but the language strongly suggests it.

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 detailed but not excessively long; each sentence serves a purpose. It could be tightened, but it remains well-structured and front-loaded.

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?

For a complex audit tool with no output schema, the description covers its purpose, failure modes, parameter usage, and when to run it. It lacks only explicit side-effect disclosure, but this is an audit tool so it's sufficient.

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

Parameters4/5

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

The schema covers most parameters (80%), and the description adds guidance on tuning and the purpose of anchorMeasure. It reinforces the schema descriptions but doesn't introduce entirely new semantics, warranting a 4 rather than 5.

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 identifies the tool as a detector for two specific failure modes when selecting slicer values, with concrete examples. It distinguishes itself from sibling audit tools by framing it as a pre-delivery reliability gate.

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?

It explicitly says 'Run before shipping' and instructs tuning parameters, giving clear usage context. It doesn't mention when not to use or alternatives, but the context is sufficiently clear.

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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