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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds real value beyond that by explaining the asynchronous polling semantics and that 'Published' means the answer is complete. It does not mention other possible states (pending/failed) or error behavior.

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?

Two tight sentences, front-loaded with the core action and followed by the meaning of the returned status. No filler.

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?

An output schema exists, so return values need not be spelled out, yet the description still usefully interprets the 'Published' state. For a simple one-parameter polling tool this is nearly complete; listing the other possible status values would close the remaining gap.

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% – the single learning_id parameter has only a UUID pattern and no description. The description partially compensates by stating the parameter is an ID returned by a knowledge query or report_knowledge_gap, which is its most important semantic, but omits format/validity details.

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 states a specific verb and resource – checking the status of a learning ID – and anchors it by naming where that ID originates (a knowledge query or report_knowledge_gap). That distinguishes it from the sibling query/report tools. It is clear, though it does not explicitly contrast with every other sibling.

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?

It gives a clear context for use (after obtaining a learning ID from a knowledge query or report_knowledge_gap) and even the follow-up step ('repeat the original query to read it'). No explicit when-not guidance, but the async workflow is fully inferable.

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