pbi_get_object

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

With no annotations, the description carries the transparency burden. It discloses that measures include DAX references and how column names should be formatted. It does not explicitly state side effects or error behavior, but the 'returns' verb implies a read-only operation.

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 brief and front-loaded with the main purpose, followed by concise parameter guidance. Every sentence adds useful information, with no waste or redundancy.

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 is relatively simple and an output schema exists, so return details are covered. However, the missing explanation of the 'source' parameter and the absence of usage guidance relative to alternative tools leave the description incomplete for fully informed selection and 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 description coverage is 0%, so the description must compensate. It explains the 'kind' values and the name format for columns, but it completely omits the 'source' parameter, leaving its meaning and use unclear. Thus the description adds partial value over the schema.

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 it returns a model object with full detail, and it specifies the admissible kinds (table, column, measure). This makes the purpose understandable, though it does not explicitly contrast with sibling list/dependency tools.

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 provides practical guidance on how to specify columns ('Tabla[Columna]') and notes that measures include their DAX references. However, it does not mention when to prefer this tool over alternatives like listing tools or dependency tools.

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