pbi_validate_measures

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

With no annotations, the description carries full burden. It discloses the key behavioral trait: 'SIN modificar el modelo (dry-run con DEFINE MEASURE)' ensures no model mutation. It also describes the return format: 'Devuelve por cada una: valid, value (muestra) y error' (returns for each: valid, sample value, and error), providing transparency about outputs. The ability for measures to reference each other is also noted.

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, three sentences, with no redundant information. It front-loads the core purpose, then gives usage context, parameter format, and return behavior. Every sentence adds value.

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?

For a single-parameter tool with an output schema, this description is complete. It covers what the tool does, when to use it, the parameter structure, behavior (no modification), and return details. No significant gaps remain for a validation tool.

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 coverage is 0%, so the description must compensate. It explains the `measures` parameter as 'lista de {"name","dax","table"(opcional)}' and adds that measures can reference each other. This gives clear meaning to the otherwise opaque schema, specifying the structure and optional fields.

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's purpose: 'Valida DAX de medidas SIN modificar el modelo' (validates DAX of measures without modifying the model). It specifies the action (validate), resource (DAX measures), and scope (dry-run with DEFINE MEASURE), distinguishing it from sibling tools like pbi_create_measure (which creates measures) and pbi_run_dax (which runs arbitrary 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?

Explicitly indicates when to use this tool: 'Ideal para probar medidas ANTES de crearlas con pbi_create_measure' (ideal for testing measures BEFORE creating them with pbi_create_measure). This names the alternative tool and provides a clear use case, effectively guiding selection among siblings.

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