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

Goes well beyond the readOnlyHint/openWorldHint annotations with cost mechanics (worst case held first, only calls made charged, failed calls free, per-call pricing in credits), the keyless-assistant fallback to not_configured, and the fact that metrics rather than raw answers are returned. These are real operational traits an agent could not infer from the structured fields.

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 what the tool does before the engine/samples/cost caveats, and every sentence carries information (engine routing, sampling semantics, cost and failure behavior). It is dense and long, but not padded or repetitive.

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 8 parameters, no output schema, and a non-trivial billing model, the description is nearly complete: it describes the returned metrics (mention, rank, citations, domains) and the billing edge cases. It omits return-shape specifics such as metric field names or pagination/limits per engine, which is a minor gap given no output schema exists.

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 baseline is 3, but the description adds meaning: it explains that `samples` exists to produce a mention rate and that `engine` accepts only the three named non-default assistants, and it frames `brand`/`competitors`/`brand_domain` as the tracked entities. Parameter usage gains real context over the schema text.

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?

Names a specific verb (ask a buyer's question to an AI assistant searching the live web) and a specific resource/output (brand + competitor mentions, rank, citations, cited domains). This is clearly distinct from the SERP/extraction siblings (google_serp, keyword_ideas, extract_local_places), so an agent can route without opening a schema.

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?

Gives concrete operating conditions: the assistant defaults to ChatGPT unless `engine` names claude/perplexity/gemini, and `samples` (1-5, default 1) is for computing a mention rate. It also explains the failure/cost path (not_configured answers cost nothing). It stops short of naming an explicit alternative sibling or stating when-not to use it.

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