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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently states the side effect (set is_published=true), the validation condition (all lessons have content_url), and the failure behavior (returns error if any lesson is empty), plus a recovery path. It omits details like idempotency or success response, but core behavior is well-covered.

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 extremely concise: two sentences, front-loaded with the main action, and the second sentence adds a concrete error-handling instruction. Every word earns its place with no redundancy.

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 tool with no output schema, the description covers the action, validation, error condition, and diagnostic fallback. It does not mention the success response format, but that is not critical for this simple publish operation. Overall, the description provides sufficient context for effective tool selection and invocation.

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 schema has one required 'id' field with 0% description coverage. The description does not explicitly elaborate on the parameter, but the phrase 'Publish a course' makes it clear that 'id' refers to the course identifier. With only one parameter and a straightforward schema, this is adequate but does not add extra meaning beyond 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 clearly states the tool's purpose: publishing a course by setting is_published=true after validating that every lesson has a content_url. This distinguishes it from create/update tools and specifies its unique role in the course lifecycle.

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 when to use this tool (when lessons are ready to be published) and explicitly directs the user to hivelearn_get_course_structure on error, providing a diagnostic alternative. However, it does not explicitly state prerequisites or situations where this tool should be avoided.

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