pbi_column_dependencies

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

No annotations are provided, so the description carries the full burden. It discloses the types of dependencies checked and the outcome ('dice si algo se rompe' - tells if something breaks), adding some behavioral context. However, it does not mention whether the operation is read-only, potential performance implications, or the return format, leaving gaps.

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 exceptionally concise: two sentences, no fluff. The first sentence front-loads the main purpose, and the second provides a practical usage tip. Every word earns its place.

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 description conveys the core purpose and a key use case, which is sufficient for a simple analysis tool. However, the complete absence of parameter explanations (especially 'source') and the lack of annotation coverage leave an agent uncertain about input semantics. An output schema exists, so return values are not needed in the description, but the parameter gap reduces overall completeness.

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% and the description does not explain any parameters. It only refers to 'column' in the purpose, but does not clarify 'table', 'column', or especially 'source' (which has a default of 'live'). The description completely fails to compensate for the lack of schema-level parameter documentation.

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 function: it identifies what uses a column (measures, calculated columns, relationships, hierarchies). The verb 'usa' (uses) and resource 'columna' (column) make the purpose specific, and it distinguishes itself from the sibling tool pbi_measure_dependencies by focusing on column dependencies.

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 explicit usage context: 'Util antes de ocultar o eliminar una columna' (useful before hiding or deleting a column). It clearly indicates when to use the tool, though it does not explicitly mention alternatives or exclusions, so it stops short of a full 5.

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