pbi_create_calculated_table

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

The description goes beyond the minimal annotations by disclosing that column inference executes the DAX against the open model and that the tool writes TMDL requiring a closed project. This adds meaningful operational context. However, it does not explain behavior of the 'overwrite' parameter or potential side effects of executing the expression.

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 but information-dense, with each sentence adding value: use case, technical mechanism, and prerequisite. No fluff or repetition, and key details are front-loaded.

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 complexity, the description covers essential operational constraints (model state, project state) and the core behavior. It does not describe return values, but an output schema exists. Missing details about overwrite semantics and error conditions are gaps, but overall it is fairly complete for a knowledgeable agent.

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 description coverage is 0%, so the description carries full burden. It explains the 'columns' parameter well (inferred by executing DAX if not provided), but leaves 'overwrite', 'description', and 'request_id' unexplained. With 6 parameters and only one meaningfully detailed, compensation is incomplete.

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 starts with a specific verb+resource: 'Crea una tabla calculada (DAX) en el modelo .pbip.' This clearly distinguishes it from siblings like pbi_create_calculated_column or pbi_create_measure. The use-case explanation (unpivoting columns to rows) further clarifies its unique role.

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 provides a concrete use case ('diez metricas guardadas en diez COLUMNAS en vez de en filas') and explicit operational requirements (model open/selected for column inference, project closed for TMDL write). While it doesn't name alternative tools, the context makes appropriate usage clear.

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