Track brand visibility in AI answers

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

Goes well beyond the annotations (readOnly, openWorld, non-idempotent) by disclosing billing implications, the APIFY_TOKEN requirement, per-vendor API key consumption billed to the key owner, and max_charge_usd protection. It also explains failure behavior precisely: invalid input yields a free row with error_reason starting with input_invalid, and a failed run returns an error carrying the run ID.

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 purpose, then inputs, then cost/auth, then return shape — a sensible order with no filler sentences. It is a single dense block that repeats a few schema details (env var names, default sample count), slightly longer than necessary but every clause carries information.

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 15 parameters and no output schema, the description carries the burden of explaining returns and does so concretely: sample rows with brand_named/brand_cited/first_mention_rank, question_summary rows with rates and a 95 percent range, and a free brand_summary row per engine. Error paths and cost/auth prerequisites are also covered, leaving no material gap 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?

Schema coverage is 100%, so every parameter is already documented with examples and defaults; the description mostly restates that (samples default 3, example question, engine key requirements). The added value is limited to reinforcing which inputs are required and how a question should be phrased, which is the baseline expectation when the schema does the heavy lifting.

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: checking whether AI answers name and cite a brand for buyer questions, across named engines. There is no sibling to confuse it with, and the first sentence alone tells an agent exactly what the tool produces.

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 clear operating context: which engines need which API keys in the server environment, that google_ai_overview needs none, default sample counts, and that Apify charges per sample and per rate row. It does not state exclusions (e.g. when not to use it), but with no sibling tools and a single obvious use case, the guidance is nearly complete.

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