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

Annotations already declare read-only, idempotent, open-world behavior, but the description adds substantial context: 30-minute refresh cadence, real non-branded buyer questions with live web search, the pre-question timing of visibility scores, a 48-hour window, and no auth or cost. This is unusually rich behavioral disclosure.

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-loads the core value ('What ChatGPT is recommending right now') and then layers in mechanism, constraints, and examples efficiently. The four sentences are compact, though the final example sentence is somewhat redundant with the opening.

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 carries the burden of explaining what is returned: brand names alongside AI visibility scores, limited to the last 48 hours. Annotations cover safety, and parameters are fully documented by the schema, so it is nearly complete, though it does not describe result ordering or structure.

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 fully documents limit and category. The description mentions category in a use case but adds no syntax, defaults, or filtering details beyond what the schema provides.

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 and action: seeing which brands ChatGPT recommends, with data refreshed every 30 minutes. It clearly describes the output and use cases, but does not distinguish itself from siblings such as aeo_score or ai_readstream.

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?

Provides implied usage through example questions ('which brands AI recommends in a category' and 'whether an AI visibility score relates to being recommended'), but offers no explicit when-to-use, when-not-to-use, or sibling alternatives.

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