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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint=false), so the description's job is to add context, and it does by disclosing the returned fields: reason, expected effect, and targeted engine. It omits freshness/recency, result limits, and whether an empty list means 'no recommendations' or 'not yet analyzed'.

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 filler, and the content of the response is front-loaded before the usage cue. Every clause 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?

With no input parameters and no output schema, the description must carry the return-value burden, and it names the three fields the agent will see. Adequate for a simple read tool, though it leaves edge cases (empty results, staleness) unaddressed.

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 tool takes zero parameters, so there is nothing for the schema to document and no description-side compensation is required. Baseline for a parameterless tool 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 resource ('everything Peak Answer currently recommends for the signed-in brand') plus the payload shape (reason, expected effect, target engine), which clearly separates it from data-oriented siblings like my_visibility or my_questions. It never names a sibling explicitly, so differentiation is implied 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 this when the user asks what to do next' gives a concrete triggering condition, which is more than most tools in this set offer. There is no guidance on when NOT to use it or which sibling to prefer for adjacent questions (e.g. raw visibility data vs recommendations).

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