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

With no annotations, the description carries full behavioral burden and does so well: it enumerates all five status values, explains current_step and course_id, and discloses the automatic failure/refund behavior for jobs stuck queued with started_at null, plus the required read:ai scope.

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?

Three sentences with no filler. The core purpose and polling trigger are front-loaded, and every sentence adds information about statuses, stuck-job behavior, or scope.

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 single-parameter polling tool with no output schema, the description covers the status model, the resulting course_id, timeout behavior, and authentication requirement. An agent has enough context to call it and interpret the response.

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%, so the description must compensate for the lone job_id parameter. It implies the source of the ID by saying to poll after hivelearn_generate_ai_course, but never explicitly states that job_id is the identifier returned by that call or by list_ai_jobs. The name is self-explanatory, but the provenance is left to inference.

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 opens with a specific verb and resource: 'Status and progress of one AI generation job.' It clearly differentiates from sibling list_ai_jobs by emphasizing 'one' job, and from generate_ai_course by framing it as the polling counterpart.

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?

Explicitly instructs when to use it: 'Poll after hivelearn_generate_ai_course.' It also states the required scope. It does not explicitly describe when not to use it or name list_ai_jobs as the alternative for multiple jobs, so it stops short of a 5.

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