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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, and no destructive action. The description adds that Anthropic probing requires a BYO key and direct payment, and that scores are returned per model. It does not mention rate limits or result variability, but overall provides useful additional context beyond annotations.

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 a single dense paragraph that front-loads the main action and then adds specific details. Every sentence adds value, with no wasted words. It is appropriately sized for the tool's complexity.

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 the tool has 4 parameters, no output schema, and returns complex per-model data, the description is fairly complete. It explains input, model options, and return structure ({score, confidence, signals, raw_response} + combined view). Missing details about aggregate scoring or confidence computation, but still adequate.

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 baseline is 3. The description adds meaning by explaining the default model, purpose of _apiKey, and how context helps disambiguate. This goes beyond the schema descriptions.

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 states the tool probes LLMs for knowledge about entities and scores visibility (0-100) per model. It specifies default and optional models, and gives concrete use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). This distinguishes it from sibling tools like ask_pipeworx or compare_entities.

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 explains when to use the tool (for visibility checks) and how to configure models (default free Workers AI, optional Anthropic with BYO key). It does not explicitly state when NOT to use it or compare to alternatives, but the use cases are clearly defined.

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