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

Annotations already declare readOnly/non-destructive/closed-world, so the safety bar is lower; the description still adds real value by disclosing the blending rule for `source` (duplicate engines counted once, CrunchJunkie scan winning), the meaning of measurementIncidents, and that pooling/null/comparison rules live in howToRead. It stops short of any rate-limit or cost note, so not a 5.

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?

Front-loaded with the daily-series purpose and then the modifiers (summary, source, incidents, howToRead). Sentences are dense but each carries distinct information; the trailing pointer to howToRead is a slight hand-off rather than self-contained content.

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 output schema, the description correctly takes on the return shape — per-point visibility, share of voice, sentiment, average position and answer counts — and explains the summary alternative. It omits the structure of dayTotals and how clientId vs projectId resolve, leaving modest gaps for an 8-parameter read tool.

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 100% and the schema already spells out defaults, caps (max 180 days, max 11 brands) and the blended/native/peec semantics. The description largely restates the same source and summary semantics, adding only that summary suppresses dayTotals — a baseline 3 given the schema is doing the heavy lifting.

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 states a specific verb and resource — day-by-day movement of AI visibility for a project — and enumerates the metric fields each point carries. It never names the sibling it is closest to (get_ai_visibility_metrics), so an agent must infer the distinction itself, which keeps it from a 5.

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?

Usage is implied through the summary flag ('summary: true returns each brand's pooled figure without the daily points'), which signals the pooled-vs-daily tradeoff. But there is no explicit 'use this when' framing and no mention of when to prefer get_ai_visibility_metrics or get_brand_perception instead.

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