get_taxonomy_branch

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

With no annotations provided, the description carries the burden of behavioral disclosure. It reveals that results are 'compact summaries' of {iri, label, definition}, warns that 'depth >2 can return very large results,' and recommends incremental navigation. This covers key behavioral traits like performance impact and return format. It falls short of a 5 only because it does not mention potential errors or edge cases (e.g., invalid branch name), but it is still notably transparent for an unannotated read 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 front-loaded with a clear one-sentence summary, followed by well-organized 'Args' and 'Returns' sections. Every sentence provides necessary information without redundancy. It is concise yet complete, using structural elements like warnings and cross-references efficiently.

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 simple two-parameter schema and the presence of an output schema, the description covers all essential aspects: purpose, parameters, return format, performance caveats, and cross-tool navigation. It provides enough context for an AI agent to use the tool correctly without missing information.

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 input schema has 0% description coverage, so the description must fully compensate. It explains branch_name with an example and directs users to list_branches(), and explains max_depth with a default, a use case (1 for top-level), and a warning about depth >2. This adds substantial meaning beyond the raw schema properties, entirely covering the parameter semantics.

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 purpose: 'Get concepts in a FOLIO taxonomy branch.' It specifies the resource (taxonomy branch) and the action (get), and distinguishes itself from siblings like get_children and get_concept by noting it returns compact summaries. This makes the tool's scope and differentiation immediately apparent.

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 guidance on when to use this tool versus alternatives: 'Use list_branches() to see all available branch names,' 'Navigate incrementally with get_children() instead,' and 'Use get_concept(iri) for full details on a specific concept.' This clearly sets usage context and alternatives, which fully satisfies the dimension.

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