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

Annotations already declare read-only, idempotent, and non-destructive hints, but the description adds crucial context: the default model (Workers AI Llama-3.3-70b) is free, Anthropic calls require a BYO key and directly bill the user, and it details the exact return structure (score, confidence, signals, raw_response + combined view). This goes well beyond the annotations.

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 two dense sentences, front-loaded with the core purpose and then covering defaults, costs, and output format. Every clause adds information, and there is no redundant or filler content.

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 a moderate 4-parameter schema and no output schema, the description compensates thoroughly: it defines the entity input, explains the default vs. optional model behavior, discloses the API key prerequisite, and specifies the return structure. It also grounds the tool in practical use cases, making it immediately actionable.

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 enriches parameter meaning by naming the specific default model ('Llama-3.3-70b') and clarifying the cost responsibility for the Anthropic key ('you pay Anthropic directly'). This adds value beyond the schema's generic 'workers-ai' and 'anthropic' strings.

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... and score visibility (0-100) per model.' It clearly states the action, target, and output. It distinguishes from sibling Q&A/research tools by focusing on visibility scoring and mentions concrete use cases like AI-marketing audits, pre-launch brand checks, and competitive monitoring.

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 gives explicit use cases: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also clarifies default behavior and when an API key is needed. However, it does not explicitly state when not to use the tool or point to alternative sibling tools, so it falls short of full when/when-not specification.

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