pbi_list_desktop_models

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

No annotations are provided, so the description carries the burden. It discloses the localhost port scanning mechanism and the specific return data, which gives a clear behavioral picture. It doesn't mention prerequisites or error behavior, but as a read-only discovery tool, it is sufficiently transparent.

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?

Two concise sentences with the main verb upfront. Every clause adds information: scope (open local models), mechanism (AS engine detection), and output (port, connection string, catalog, table count). No fluff.

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 simple read-only nature, no annotations, and an output schema present, the description covers the essential context. It could mention that Power BI Desktop must be running, but 'abiertos localmente' implies that. The return values are described, and the output schema handles structure.

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 tool has no parameters, so the baseline is 4. The description adds context about what the tool yields, making the zero-parameter interface self-explanatory.

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 lists open Power BI Desktop models and specifies the mechanism (detects AS engine via localhost) and return fields (port, connection string, catalog, table count). This differentiates it from sibling tools that list tables or measures.

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 implies the use case (enumerate locally open models) but provides no explicit guidance on when to prefer this over alternatives like pbi_select_model or pbi_list_tables. No exclusions or alternative tool references.

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