pbi_search_model

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

Beyond the readOnlyHint annotation, the description adds useful behavioral context: case-insensitive search, filtering by kinds, and a distinction for measures between name and expression matches. This enriches the agent's understanding of the tool's behavior.

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, front-loaded with the main purpose, and uses clear formatting for parameter details. Every sentence adds value, and there is no unnecessary filler.

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 presence of an output schema and read-only annotation, the description does not need to explain return values. It covers the core behavior and important nuances (case-insensitivity, kinds, name-vs-expression), though it omits details about `limit` and `source`. Overall, it is sufficiently complete for a search 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 description coverage is 0%, and the description only explains `term` and `kinds`, adding meaning like case-insensitivity and filter types. However, `limit` and `source` are left undocumented, leaving gaps that the schema does not fill.

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: searching model objects by name and within measure DAX. The verb 'Busca' (search) is specific, and the resource 'objetos del modelo' is well-defined. This distinguishes it from sibling tools like list_tables or get_object.

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 usage for finding model objects by term, but it does not explicitly state when to use this tool over alternatives like get_object or list_measures, nor does it provide exclusions. The use case is implied but not explicit.

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