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pbi_create_variance_measure

Creates a period-over-period variance measure in Power BI, comparing current period to a previous one with configurable offset and granularity (year, month, quarter).

Instructions

Create a period-over-period variance measure.

DAX template (parametrised by granularity ∈ {year, month, quarter}):

[{base}] - CALCULATE([{base}], DATEADD({date_table}[{date_column}], {offset}, {granularity}))

Default compare_period_offset = -1 ⇒ "current period vs previous one".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYes
overwriteNo
date_tableYes
date_columnYes
granularityNoyear
base_measureYes
measure_nameNo
format_stringNo
display_folderNoVariance
compare_period_offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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 discloses the DAX formula and default behavior, but does not mention side effects like overwriting existing measures, permission requirements, or other behavioral traits beyond the formula.

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 concise with a single sentence and a code block. It is front-loaded with the purpose. However, it could be slightly better structured by listing parameters or providing more explicit guidance.

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

Completeness2/5

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

Given the tool has 10 parameters and an output schema (present in context but not described), the description is incomplete. It does not mention return values or explain all parameters, making it hard for an agent to fully understand usage.

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%, so the description must compensate. It explains some parameters (base, offset, granularity, date table, date column) and gives defaults, but fails to document many others (table, overwrite, measure_name, format_string, display_folder). This leaves significant gaps.

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 'Create a period-over-period variance measure', which is a specific verb+resource pair. It distinguishes itself from sibling tools like pbi_create_measure or pbi_create_rolling_average_measure by explicitly mentioning period-over-period variance and providing the DAX template.

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

Usage Guidelines3/5

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

The description gives context about default offset and granularity options, implying a typical use case. However, it does not explicitly state when to use this tool versus alternatives like pbi_create_measure or pbi_create_topn_measure, nor does it provide when-not-to-use guidance.

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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