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

The description adds significant behavioral context beyond annotations: default model (Workers AI Llama), optional Anthropic key with cost implication, return structure (per-model {score, confidence, signals, raw_response} + combined view). Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which the description does not contradict.

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 four sentences, each adding value without redundancy. It front-loads the primary action and return format, then includes usage guidance and example use cases. No fluff or unnecessary details.

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's moderate complexity (4 parameters, no output schema, good annotations), the description explains the purpose, usage, and return structure (score, confidence, signals, raw_response). It could be more specific about the raw_response field or how confidence is calculated, but it is sufficient for an agent to select and invoke the tool correctly.

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 reiterates parameter roles (entity, models, _apiKey, context) but does not add new meaning beyond what the schema already provides. It does provide usage context (default model, API key purpose) but that is already in 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 an entity and returns a visibility score (0-100) per model. It uses specific verbs ('probe', 'score') and resource ('business/brand/product/topic'), and distinguishes from sibling tools like 'ask_pipeworx' (general Q&A) and 'deep_research' by focusing on AI visibility audits.

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 explicitly mentions use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' However, it does not provide explicit when-not-to-use guidance or direct comparisons to alternatives, though the context makes it clear this is a specialized audit tool.

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