search

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It adds useful context about defaults (e.g., 'Empty = current term' and 'Empty = current year') and specifies the shape of the output (list of codes, titles, instructors, meeting times, seat availability). However, it does not mention limits, pagination, or how filters combine, which would improve transparency further.

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 and well-structured: a two-sentence purpose statement followed by a bullet-like 'Args:' list. Every sentence provides necessary information without waste. It is front-loaded with the tool's purpose and output, making it easy to scan.

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 has 5 optional parameters and an output schema, the description is largely complete: all parameters are documented with examples and defaults, and the return type is summarized. The main gap is that the description does not explain how multiple filters interact, sorting, or result limits, but the presence of an output schema and the straightforward search nature keep it sufficient.

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 only provides titles and defaults, with 0% description coverage. The tool description compensates fully by explaining each parameter with examples (e.g., 'keyword: Free-text search, e.g. "linear algebra", "MATH 1530"') and clarifying default behavior for semester and year. This adds significant meaning beyond 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 the tool's purpose: 'Search Brown's course catalog (Courses@Brown / CAB)' and defines the output as 'a compact list of matching courses with codes, titles, instructors, meeting times, and seat availability.' This distinguishes it from siblings like course_detail (which would presumably provide full details) by emphasizing the compact list format.

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 guidance on what each parameter does (e.g., 'keyword: Free-text search... department: Department code...') and implies use for searching courses. However, it does not explicitly state when to use this tool versus its siblings (departments, course_detail, batch_course_details, schedule_check) or exclude any scenarios, so usage guidance is primarily implied rather than explicit.

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