pbi_validate_measures

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

The description adds meaningful behavioral details beyond the readOnlyHint annotation: it explicitly states the dry-run mechanism (DEFINE MEASURE), notes that measures can reference each other, and describes the return structure (valid, sample value, error). This gives the agent a clear picture of what happens during execution.

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 (3 sentences) and front-loaded with the core purpose. Every sentence adds value: purpose, intended use, and parameter structure. No repetition or extraneous detail.

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 tool's simplicity (one parameter) and the presence of annotations and output schema, the description provides all necessary context: what the tool does, how to format the input, and what the output will contain. An agent can invoke this tool correctly without additional information.

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 only defines a generic array, leaving the inner structure undocumented. The description compensates fully by specifying that `measures` is a list of objects with `name`, `dax`, and optional `table` fields, and explains cross-referencing behavior. This is essential information that the schema lacks.

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 measures without modifying the model, using a dry-run with DEFINE MEASURE. It also specifies the intended use case (probing measures before creation), which distinguishes it from related tools like pbi_create_measure and pbi_run_dax.

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?

The description explicitly says it is ideal for testing measures before creating them with pbi_create_measure, providing a clear usage context. It does not explicitly mention when not to use it or alternative validation tools, but the guidance is sufficient for an agent to select this tool appropriately.

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