pbi_validate_measures

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

With no annotations, the description carries full responsibility for disclosing safety behavior. It clearly states the operation is a dry-run and does not modify the model, and it describes inter-measure references and return fields. However, it omits any potential limitations or prerequisites, so it falls short of a perfect score.

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?

The description is concise and well-structured, starting with the core function, then usage guidance, then parameter details. It conveys essential information in three short lines with no redundant content.

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?

The tool has a complex nested parameter and no annotations, but the description covers its purpose, safety, input structure, and output format. Since an output schema exists, detailed return value documentation is not required; however, it does not mention prerequisites like a selected model, leaving a minor gap.

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?

The input schema is minimal (only an array of objects with additionalProperties), while the description fully specifies the expected structure: each object has 'name', 'dax', and optional 'table', and measures can reference each other. This completely compensates for the schema's lack of detail.

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 validates DAX expressions of measures without modifying the model (dry-run with DEFINE MEASURE). It explicitly positions itself relative to pbi_create_measure, distinguishing its non-destructive validation purpose.

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?

It explicitly recommends using this tool before creating measures with pbi_create_measure, providing a clear alternative and when-to-use context. This gives direct, actionable guidance for an agent deciding between tools.

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