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add_auto_aggregations

Create hidden aggregate tables and rewrite measures to query pre-summarized data, improving Power BI performance.

Instructions

Build an auto-aggregation scaffold: a hidden {Table}_Agg GROUPBY table over the group-by columns, hidden _Agg measures for each mapping, and IF-routing rewrites of the base measures so they answer from the small agg table when possible. groupByColumns is comma-separated Table[Column]. measureMappings is comma-separated aggMeasureName=baseMeasure. Extends set_aggregation. FLAG: the IF-routing is a best-known scaffold - review before production.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sessionIdYes
detailTableYesthe detail (fact) table to summarise
groupByColumnsYesgroup-by columns, comma-separated Table[Column]
measureMappingsYesmeasure mappings, comma-separated aggMeasureName=baseMeasure
Behavior4/5

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

With no annotations provided, the description carries the transparency burden; it discloses that it creates hidden objects, rewrites base measures, and only routes 'when possible.' The explicit FLAG warning that the IF-routing is a 'best-known scaffold' and needs review before production is valuable beyond the schema. It stops short of detailing side effects on existing aggregation tables or reversibility, but the core mutating behavior is clear.

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 and content-dense: two sentences plus a needed flag. It front-loads the core purpose and provides necessary parameter formats, but the first sentence is long and packs many clauses; still, no words are wasted.

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?

For a complex mutation tool with no annotations and no output schema, this description gives a strong overview of what will be built, the parameter syntax, and a production risk note. It leaves minor gaps (prerequisites, reversibility, return value), but the core context is present.

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 description reaffirms and clarifies the comma-separated formats for groupByColumns and measureMappings, matching and slightly expanding the schema. It provides context on how the parameters map to the scaffold (hidden measures per mapping), though it does not elaborate beyond the schema for detailTable or sessionId.

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 opens with a specific verb ('Build') and resource ('auto-aggregation scaffold'), then enumerates the exact deliverables: hidden agg table, hidden measures, and IF-routing rewrites. It explicitly distinguishes itself from the sibling 'set_aggregation' by stating it extends that tool, clarifying its niche.

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 phrase 'Extends set_aggregation' signals a relationship to an alternative, but the description never explicitly states when to choose this tool over set_aggregation or when not to use it. It implies usage for auto-aggregation but lacks clear exclusions or prerequisites (e.g., whether set_aggregation must be run first).

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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