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

Annotations already cover read-only safety, and the description adds genuinely non-obvious context: gaps are hand-authored, derivation produces none, so an empty result is not proof of completeness. It does not, however, mention ordering, pagination, or freshness of the inventory.

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 tight sentences, front-loaded with the definition, then the interpretation warning. The middle sentence slightly overlaps the first but each still earns its place.

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 read-only inventory tool with one optional parameter and no output schema, the description supplies the essential framing and the key trap (empty != no gaps). Only the when-to-call routing relative to query/search is missing.

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 description coverage is 100% for the single optional 'model' parameter, which already explains it comes from list_models and defaults when omitted. The description adds nothing about model scoping, so baseline 3 applies.

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?

States a specific verb and resource ('List questions this data CANNOT answer') and clarifies the semantics against a common confusion: a nearby metric answers a different question. This is clearly distinguishable from siblings like query or search.

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 tells the agent how to interpret the output but never states when to call this tool versus alternatives such as query or search (e.g., 'call this before telling a user the data cannot answer something'). Usage is implied rather than spelled out.

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