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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds valuable behavior: it explains the free default model (Workers AI Llama-3.3-70b), the BYO-key mechanism for Anthropic with direct cost implications, and the return structure {score, confidence, signals, raw_response} + combined view. No contradiction with annotations. It adds context beyond annotations without over-explaining.

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 primary purpose, then details the default model/optional key, then the return format and use cases. Every sentence adds necessary information with no filler or repetition.

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 params, no output schema), the description covers the essential aspects: what it does, how to configure models, return format, and typical use cases. It lacks minor details like rate limits or score computation methodology, but these are not critical for selecting/invoking 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 coverage is 100%, so baseline is 3. The description adds some extra context by explaining that the default model is free and that _apiKey is for Anthropic, but it largely reiterates what the schema already states (e.g., _apiKey is 'Optional Anthropic API key'). No major enrichment beyond 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 opens with a specific verb and resource: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' This precisely states the tool's purpose and differentiates it from siblings like ask_pipeworx or deep_research by focusing on visibility scoring across models. The mention of per-model scoring and a combined view further clarifies its unique role.

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 contexts: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' However, it does not explicitly state when not to use it or name alternative tools for similar tasks, so it stops short of the 'explicit when/when-not' level.

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