dataverse_aggregate_table

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds valuable behavioral context: works on up to 50,000 records, lookup fields cannot be used in groupby, and correct use of 'countdistinct'. No contradictions.

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

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

Four sentences, front-loaded with purpose, each sentence adds value. No unnecessary words. Efficiently communicates key information.

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

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

Given the presence of an output schema (not shown) and thorough annotations, the description covers usage context, constraints (50k limit), and examples. No missing critical information for correct invocation.

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

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

Schema describes all parameters with coverage 0% (per context), but each parameter has a description in schema. Description adds examples for the apply parameter but does not elaborate on other params. Baseline 3 is appropriate as schema already provides meaning.

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

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

The description clearly states the tool groups and aggregates Dataverse records using OData $apply. It explicitly distinguishes from siblings: dataverse_count_records for single total count and dataverse_query_table for raw rows.

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

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

Provides clear when-to-use (per-group questions like count by status, sum by region) and when-not-to-use (single total count or raw rows), with explicit sibling tool alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.