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

The annotations already establish read-only and non-destructive behavior, and the description adds meaningful behavioral context: credit costs per platform, quote-first handling for expensive calls, free repeats for known-empty lookups, default platform fetching in the US, and the fact that answers are truncated and structured-content-only. This goes well beyond the annotation surface.

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 every sentence carries information: core output, cost model, quote requirement, and return behavior. It is front-loaded with the primary purpose and uses no filler or repetition beyond what is operationally necessary.

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?

Despite having no output schema, the description tells the agent what is returned (top limit prompts by AI search volume, structured-only and truncated) and covers the non-obvious workflow (quote confirmation). Together with 100% parameter schema coverage, this is sufficient for correct invocation.

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?

With 100% schema description coverage, the baseline is 3, but the description materially adds meaning: it explains per-platform credit costs, US default fetching of both Google and ChatGPT, ChatGPT availability limited to 'us', and the quote/confirm workflow for confirm_quote_id. This helps the agent choose size, country, and platforms correctly.

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 clearly defines what the tool returns: AI prompts whose answers cite a domain, along with platform, search volume, cited URL, and source rank. It also specifies 'one list per platform,' which distinguishes it from sibling tools like ai_sources or ai_overview without requiring the agent to open schemas.

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?

The description gives operational guidance (costs, quote flow, defaults) but never states when to use this tool versus alternatives such as ai_sources, ai_overview, or ai_check. There are no explicit exclusions or sibling comparisons, so an agent must infer the right context from the tool name and title.

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