list_topics

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

No annotations exist, so the description carries the full behavioral burden, yet it only hints at output contents ('counts and grouping notice'). It says nothing about what 'suggested' implies (heuristic grouping? freshness?), whether the listing is scoped or permission-bound, or how the optional day influences results.

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?

One front-loaded sentence with no filler, and the key scope phrase ('across apps') comes early. It could have spent a few of its remaining words on the day parameter or exclusion guidance.

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?

With no annotations, no output schema, and an undocumented parameter, the description is too thin for an agent to invoke this confidently. The overview of the return shape is a start, but the day semantics and the distinction from sibling topic tools are 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 coverage is 0% and the single 'day' parameter is never mentioned in the description. The agent cannot tell whether day filters, defaults to today, or accepts a format like ISO date versus a relative label, despite the empty-string default.

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 suggested project/topic groups across apps,' plus the payload shape (counts, grouping notice). It is distinguishable from read_topic (singular detail fetch) and list_apps, though it never names those siblings explicitly.

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?

No when-to-use guidance at all. Nothing tells the agent whether to call this before read_topic, how it relates to list_apps, or what 'suggested' means in contrast to a raw topic list.

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