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 context beyond these: the default model is free, and passing `_apiKey` means 'you pay Anthropic directly,' disclosing cost implications. It also clarifies the return payload. 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, front-loaded with the core function and output, followed by pricing/alternative details and use cases. Every sentence provides necessary information without wordiness. It's an exemplary compact description.

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 there is no output schema, the description appropriately includes return format: 'per-model {score, confidence, signals, raw_response} + a combined view.' It also covers default behavior, optional key, and typical use cases. Minor gaps like error handling or scoring methodology prevent a 5, but it is quite complete for a read-only probe 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 all four parameters have clear documentation. The description adds extra meaning by explaining the default model and the conditional need for `_apiKey` only when 'anthropic' is in models, which the schema's per-parameter description doesn't fully convey. This elevates it above the baseline of 3.

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 opens with a specific verb+resource: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' This clearly distinguishes it from sibling tools like ask_pipeworx or scan_competitor_ai_presence by emphasizing per-model visibility scoring. It also provides concrete output structure (score, confidence, signals) and use cases.

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 states 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring,' giving clear contexts for when to use it. It also explains the default vs. optional Anthropic probe, which helps decide whether to bring an API key. However, it does not explicitly name alternatives or when not to use, so it falls slightly short of a 5.

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