list_topics

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

No annotations are provided, so the description bears the full disclosure burden. It reveals the payload fields but says nothing about read-only safety, ordering, pagination, or whether the list is exhaustive — meaningful for a tool billed as 'All topics'.

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 sentences, no waste, with the returned content front-loaded and the use case second. Every sentence 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?

An output schema exists, so return-value explanation is not required, and a zero-parameter read tool needs little else. Only ordering/scale behavior is left unstated, which is a minor gap given the low complexity.

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 defines zero parameters, which is the baseline-4 case; there is nothing for the description to disambiguate. The description correctly implies the call takes no scoping arguments.

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 the resource (all topics) and what each entry carries (claim counts and creators), which separates it from get_topic (single topic) and merge_topics. It stops short of naming those siblings explicitly, so the distinction is inferable rather than stated.

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?

'Use it to plan clean topic names and merges' gives one concrete use case, implying this is the discovery step before merge_topics or renaming. There is no when-not guidance and no explicit pointer to an alternative for other needs.

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