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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: the default model is free, passing _apiKey enables Anthropic calls with direct payment, and it returns a per-model structure. This goes beyond the annotations without contradicting them.

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 three sentences with a clear flow: primary action, default/pricing caveat, return format, then use cases. No filler or redundancy; each sentence adds necessary 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?

Without an output schema, the description compensates by including the return shape (per-model {score, confidence, signals, raw_response} + combined view). It also covers pricing, key usage, and typical applications. Minor gaps, such as what 'signals' means or how scores are computed, are understandable for a read-only tool with strong annotations.

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. The description enhances parameter understanding by explaining default model behavior and how _apiKey is passed straight through to api.anthropic.com, including payment implications. This adds meaning beyond the schema's individual parameter 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's function: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' This uses a specific verb (probe) and resource (LLMs), and the output (visibility score) differentiates it from sibling tools like entity_profile or ask_pipeworx.

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 provides clear use context: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not explicitly mention when not to use it or name alternative tools, but the context is concrete enough for an agent to understand typical scenarios.

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