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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds meaningful context beyond annotations: it explains the default model (Workers AI Llama-3.3-70b), billing implications of passing _apiKey, and the exact return structure (score, confidence, signals, raw_response). No contradiction exists between annotations and description.

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 serving a distinct purpose: purpose, default/API key, return format, and use cases. It is front-loaded with the core function and contains no fluff or repetition. Every sentence earns its place.

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?

With no output schema, the description compensates by laying out the per-model return fields and the combined view. It covers model selection, auth/billing, and examples of use. A minor gap is lack of error/edge-case behavior (e.g., rate limits or unexpected responses), but overall it is sufficient for an agent to 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 adds extra meaning to parameters, particularly _apiKey (BYO key, direct payment) and models (default is workers-ai, anthropic optional). It also clarifies that entity/context are used for the probe, reinforcing schema descriptions without redundancy.

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: probing one or more LLMs and scoring visibility (0-100) per model. It distinguishes itself from sibling tools by focusing on multi-model visibility scoring rather than simple Q&A or research. The phrase 'Probe one or more LLMs' is a specific verb+resource that leaves no ambiguity.

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 lists use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also provides context on when to provide an optional Anthropic API key (to include Anthropic, with direct billing). However, it does not mention when not to use the tool or name specific alternative tools, so it lacks explicit exclusions.

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