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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so safety is covered. The description adds genuinely new behavior: data is 'collected once a day', ranges are UTC days, and results are compared 'against the period of the same length before' — useful for interpreting freshness and deltas. It stops short of stating auth requirements or what the settings view implies about permissions beyond 'whether this account may change them'.

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, front-loaded with the core purpose before the metric enumeration, and the view-routing hint lands at the end where it is actionable. The metric list ('visibility, share of voice, sentiment, average position and use as a source') is dense but each item is meaningful, not 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?

With an output schema, full annotation coverage and 100% schema description coverage, the description only needs to add orientation, which it does: it explains collection cadence, the UTC day semantics and the comparison window. Minor gaps remain around empty-data behavior and answer pagination, but nothing required to invoke it correctly 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%, so the schema already documents from/to defaults, the view enum, limit bounds and answer_id. The description largely restates the view meanings and the date-range comparison rather than adding syntax or edge-case rules (e.g. what happens if from > to). Baseline 3 is appropriate when the schema carries the parameter burden.

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 names a specific verb and resource: 'Read how AI assistants talk about the brand of a Flowlines namespace', then concretizes it with the surfaces (ChatGPT, Gemini, Google AI Overviews) and the metric set. It is clearly distinguishable from read siblings like get_namespace_overview, but it never mentions the write counterpart manage_ai_visibility, so the read/write split is left implicit.

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 an explicit routing rule between views: 'Use answers and answer for the evidence behind a number', which tells the agent when to escalate from aggregate numbers to raw answers. It does not state when to prefer this tool over manage_ai_visibility or what to do when a namespace has no runs yet, so exclusions are absent.

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