list_course_labels

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

Annotations already mark this read-only/idempotent/non-destructive, and the description adds meaningful behavior: labels attach to the content, not the publication; they are consistent across domains; they are internal and do not affect learner-facing output; and a special credential is required. This is useful context beyond what annotations provide.

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?

Each sentence carries relevant information: purpose, course-vs-publication semantics, internal visibility, and credential requirement. The description is compact and well-structured, though the leading noun-phrase style makes the purpose slightly less direct than a verb-led sentence would be.

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?

For a one-parameter, read-only tool with an output schema, the description covers all essential operational aspects: what is returned, how course_id should be interpreted, scope of the result, and the required credential. Nothing important is missing for an agent to invoke it correctly.

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 coverage is 0%, and the description significantly compensates by explaining the crucial distinction between a course and a published course and clarifying that labels are attached to the content entity. It does not explain the string format or how to obtain the course_id, but for a single required parameter this is strong semantic guidance.

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 resource ('a single course') and the kind of data returned ('internal classifications'), and distinguishes these labels from published-course-level data. It lacks an explicit verb like 'list' or 'retrieve', and does not directly contrast itself with the sibling list_labels tool, so it stops short of a 5.

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 gives useful context: course_id refers to a course, not a published course, and labels are domain-independent and internal. However, it never states when to prefer this tool over alternatives such as list_labels, list_tags, or list_group_categories, and offers no exclusion criteria.

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