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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world behavior. The description adds substantial operational context: latest versus earlier run comparison, mention/citation rates, excerpts, answer ids, tracked-sample limitations, no causation claims, and expected latency.

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?

The description is dense but front-loaded and every sentence carries operational value, covering purpose, matching behavior, return fields, next steps, limitations, and latency. Its length is justified because there is no output schema and the tool has non-obvious result semantics.

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?

Given no output schema and rich behavioral requirements, the description is complete: it explains what is returned per engine, the comparison window, excerpts, answer ids, matched prompts, other brands, and why results are not exhaustive. It also tells the agent how to handle zero matches and what not to claim.

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%, so all four parameters are documented in the schema. The description adds useful meaning beyond the schema by explaining that the question is matched to tracked prompts in any language and that results are limited to tracked engines and prompts, though it does not deepen engine or lookbackDays semantics much further.

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: it answers whether the brand appears in AI search for a topic using stored visibility results, including tracked prompts and engines. It distinguishes itself from visibility_get_answer by routing full-text needs there and from new audits by saying it reads stored results only.

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?

Explicitly says to pass the user's question or topic in their own words, call visibility_get_answer when the user wants full answer text, and tell the user when no tracked prompt matches. It also clarifies the when-not case: it does not run new checks, so it should not be used when a fresh audit is required.

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