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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds genuinely new context not in structured fields: the data source is sampled from Google Gemini and OpenAI's open-weight GPT-OSS models, which shapes how a consumer should interpret the results. It stops short of disclosing freshness, sampling size, or rate limits.

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 dense sentence plus a short sourcing sentence; front-loaded and mostly waste-free. Enumerating all seven aspect values duplicates the schema enum, which is the only mild bloat.

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 never indicates what the response contains (per-aspect text? scores? sources?) beyond the aspect names. For a read-only analytical tool this is acceptable but leaves a real gap in knowing what comes back.

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% and the aspect enum is fully documented in the schema, so the description's restatement of the seven aspect values is redundant. It adds nothing about the domain parameter or how aspect values differ semantically. Baseline 3 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 verb+resource: how LLMs describe a brand when asked shopping questions, broken out by aspect. The domain/aspect framing distinguishes it from near-siblings like get_ai_model_citation_rate or get_ai_agent_readiness_score, though it never names those alternatives directly.

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?

There is no explicit when-to-use or when-not-to-use guidance and no mention of alternatives among the many AI-visibility tools (citation rate, readiness score, shopping agent checkout race). Usage must be inferred from the aspect list.

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